The 2013 Dragon 3 cooperation brochure presents the activities undertaken since the formal start of programme

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2 THE 03 DRAGON 3 BROCHURE list of istitutions DRAGON 3 The 03 Dragon 3 cooperation brochure presents the activities undertaken since the formal start of programme in June 0. The ESA-MOST Dragon 3 new cooperation objectives are to promote the exploitation of ESA and Chinese EO data for science and application development, to stimulate scientific exchange by the formation of joint Sino-European teams, to provide training to young European and Chinese scientists and to publish co-authored results. The Dragon 3 cooperation will last 4 years and involves 5 projects to conduct land, ocean and atmospheric investigations using ESA, Third Party Missions and Chinese EO data. There are 700 scientists from 70 European/ Chinese research institutes involved in the joint research projects. The joint teams have formally kicked off their projects at the first Dragon 3 Symposium which took place from 8 and 9 June 0, in Beijing, China. ESA and NRSCC have organized a progress meeting in October 0 in Beijing. At this meeting, Chinese scientists provided details about their project progress and further defined their EO data requirements. For ESA and Chinese EO data, detailed coordination of all requested acquisitions over China is being performed by ESA and NRSCC respectively. The first Dragon 3 advanced land remote sensing training course was successfully held on 5 to 0 October 0 hosted by the National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, CAS, Beijing China. The course was attended by 70 MSc. Ph.D. and post-doctoral level trainees. The course was taught by 3 European and 9 Chinese leading Dragon 3 scientists with expertise in optical, thermal and microwave land remote sensing. The course lectures covered theory of land RS and the practical sessions introduced some applications exploiting data from ESA and China EO missions. The next Dragon 3 annual symposium is to be held in Palermo Italy in June 03 at which the projects results will be presented for the 5 projects. In addition Dragon 3 young scientists will be in attendance and will take part in project team meetings and make plans to work with Chinese scientists and institutes as part of their studies. The next steps during 03 are the preparation for the advanced training course in ocean remote sensing that will be held at the Chinese University of Hong Kong, P.R. China in October 03. Finally the Dragon 3 joint website has been prepared and is the formal reporting portal and information about the programme, partners, projects executive summaries, satellites, instruments and study areas can also be found. (see We confirm that the Dragon 3 cooperation has got off to an excellent start and we thank all of the Sino-European investigators for their contribution to this 4 year programme. We look forward to working with you and the results reporting in the coming months. Best regards, The Dragon 3 cooperation coordinators, ESA - Yves-Louis Desnos, yves-louis.desnos@esa.int NRSCC - Li Zengyuan, zengyuan.li@caf.ac.cnthe 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 3

3 ABOUT THE DRAGON 3 cooperation list of istitutions programme DRAGON 3 Background ESA, together with the National Remote Sensing Centre of China (NRSCC), an entity under the Ministry of Science and Technology (MOST) of the P.R. China, have cooperated in the field of Earth observation application development for more than 7 years. In 004, a dedicated four year science and exploitation Dragon programme was initiated in 6 priority areas using ESA ERS and Envisat data in P.R. China. This was followed in 008, by the Dragon programme which lasted 4 years and under which there were 5 geo-science projects. In 0, this cooperation was further expanded to a third phase with 5 projects exploiting ESA, TPM and Chinese EO data for land, ocean and atmospheric science and application development in P.R. China. The third phase of the Dragon cooperation will last for 4 years and will finish in 06. Objectives The Dragon 3 cooperation is targeted towards land, ocean, cryosphere, geodesy, climate and atmospheric investigations in P.R. China. The expected benefit and contribution is to: Promote the use of ESA, TPM and Chinese EO data for science and application development in China Stimulate scientific exchange in EO science and application by the formation of joint Sino-European teams Publish the co-authored results of the research and applications development at the mid term stage and at the end of the programme Provide training to young scientists in EO data exploitation for science and applications. Project Themes The Dragon 3 cooperation web site Dragon Final Results and Dragon 3 KO Symposium 6 0 Advanced Land Training 7 ESA Processing Tools 8 Academic Exchanges 9 Dragon 3 Cooperation Management 0 Dragon 3 Young Scientists ESA, TPM and Chinese EO Satellites and Instruments ESA and TPM EO Data Delivery 4 Chinese EO Data Delivery 5 Dragon 3 Study Areas 6 Access to EO Data 7 Dragon 3 Web site 8 Dragon Upcoming Events 9 PROJECTS Land & Environment ID Desertification 0 ID. 050 MONITOR ID. 055 Epidemics Monitoring ID Land Subsidence Monitoring 3 ID Forest DRAGON 3 4 ID Forest Modelling 6 ID Urban Development & Climate 7 Renewable Resources ID Farmland Drought 8 ID Forest Change Monitoring 9 ID Crop Production Estimation 30 ID Crops Monitoring 3 ID Forest Resources Research 3 ID LU Change & Water Quality 33 Hazards ID Forest Fires & Emissions 34 ID Wetlands Monitoring 35 ID Landslides Monitoring 36 ID Crustal Deformation & Infrastructures 37 ID Terrain Motion & Landslides Case Studies 38 ID. 067 Seismic Anomalies Detection 39 ID Seismology 40 Hydrology ID Water Cycle & River Basins 4 ID Yangtze River Basin Hydrology 43 ID Hydrology Products 44 ID Dongting Lake Flood Dynamics 45 CAL/VAL ID. 053 Atmospheric Dynamics from LIDAR 46 ID. 06 SMOS CAL/VAL & Soil Moisture 47 POLSAR & InSAR ID POLINSAR 48 ID Terrain Measurement 49 Geodesy ID. 059 Geoid & GOCE 50 ID GSM4GCM 5 Cryosphere ID. 030 Himalayan Glacier Dynamics 5 ID. 050 Sea Ice Monitoring 53 ID. 06 Glaciers & Hydrological Dynamics 54 ID Cryosphere Dynamics Tibetan Plateau 55 Atmosphere & Climate ID East Asia Monsoon & Air Quality 56 ID. 059 Atmospheric Dynamics & Cities 57 ID. 056 CO Assessment in Ecosystem 58 ID Chemistry Climate Change 59 ID CO from Space 60 ID AMFIC 6 ID CEOP TPE 6 Coastal Zones ID Coastal Zones 64 ID Monitoring Yangzte River Mudflats 65 ID Land, Sea Ground Water Exchange 66 ID Data & Models Synergy for Coastal Dynamics 67 ID Reclaimed Land Monitoring 68 ID OPAC 69 Oceanography ID. 04 Ocean Resources & Microwaves RS 70 ID Applications of RA data 7 ID Marine Safety &Security 7 ID Oil Spill Monitoring 73 Projects References 74 List of institutions in the Dragon 3 cooperation 78 4 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 5

4 DRAGON 3 programme programme DRAGON 3 Dragon Final Results and Dragon 3 Kick Off Symposium 0 Advanced land training course Date 5 9 June 0 Date 5 to 0 October 0 Venue Hosts China People s Palace Hotel, Xicheng District, 00045, Beijing NRSCC and ESA Hosts ESA, NRSCC and MITL IECAS - National Key Laboratory of Microwave Imaging Technology Institute of Electronics, Chinese Academy of Sciences, Beijing, China Participants 400 Chinese and European scientists Lecturers senior scientists in optical, thermal and microwave land applications 0 Dragon Symposium poster 0 training course poster Main objectives of the Symposium:. Dragon Final Results Reporting The first three days of the 0 Dragon symposium served as the teams final reporting of their Dragon Programme results from 008 to 0. All of the teams were requested to submit their final papers for the Symposium proceedings (ESA SP-704). On a project-by-project basis, 4 project teams reported on their results of exploitation of ESA, TPM and Chinese EO data acquired during the course of the Dragon Programme. Following the Symposium, the proceedings were published as a joint publication and are available on DVD. Poster Session for Young Scientists On the afternoon of the second day, there was a special poster session dedicated to research by the young scientists. Prizes were awarded for the best poster papers in land, ocean and atmospheric EO science. The posters were judged by Dragon lead investigators. A total of 65 poster papers were presented. The posters presented the results of research using ESA, TPM and Chinese EO data in P.R. China.. Kickoff for the Dragon 3 Projects The last two days of the Symposium served as the formal kick off for Dragon 3 projects. EO data delivery commenced following the Symposium and will last for four years. Presentations were made on the 5 successful projects from the Dragon 3 Announcement of Opportunity (AO). This AO concerns the exploitation of ESA, Chinese and TPM EO data for science and applications development in P.R. China. All of the project executive summaries were published in English and Chinese and were distributed at the Symposium. ESA and NRSCC signed the protocol agreement which covers EO data exchange, training and organization of reporting and publication of results during the 4 years of the programme. In each year of the programme, annual Symposia will be held alternatively between China and Europe. In each year of the programme, advanced training courses in land, ocean and atmospheric applications will be held in China. The results will be published at the mid term and at the end of the programme as Symposium proceedings. Ph.D. Lorem students, ipsum postdoctoral dolor sit and amet, research consectetur scientists interested adipiscing in land elit. remote Praesent sensing ac quam from China id and arcu other tempor Asian countries pellentesque. were invited In hac to a habitasse 6 day training platea course dictumst. organised In within vel ipsum the framework nisi. Proin of the et sapien Dragon 3 ac Cooperation. mi laoreet The condimentum course was vehicula hosted by vitae MITL est. IECAS, Aenean Beijing, fringilla China. The goals were to provide theory and practical sessions on remote ante non ligula commodo ultricies. Curabitur accumsan tortor sensing for land applications. in eros dignissim id tincidunt lectus sodales. Duis congue There sodales were augue, 9 Chinese ut hendrerit and 3 European mauris lecturers condimentum who gave sed. lectures Nunc on: ac nisi Current augue. and Vivamus future European, ante justo, Third tempor Party et Mission tincidunt and et, Chinese placerat EO eget satellite lectus. missions Ut fringilla and access blandit to EO ipsum, data sit amet porttitor augue posuere ac. Principles of optical, thermal, active and passive microwave remote Lorem sensing ipsum for land dolor applications sit amet, consectetur adipiscing elit. Praesent ac quam id arcu tempor pellentesque. In hac EO data processing and products demonstration for: flood mapping habitasse and lakes platea monitoring; dictumst. dry land In vel crop ipsum mapping; nisi. land Proin use et sapien change ac detection; mi laoreet forest condimentum mapping and vehicula parameter vitae retrieval; est. Aenean terrain fringilla motion; ante non ligula commodo ultricies. Curabitur accumsan tortor in water eros resources dignissim assessment; id tincidunt glacier lectus mapping sodales. & snow Duis parameter congue sodales retrieval; augue, the assessment ut hendrerit of urban mauris development condimentum sed. Nunc ac nisi Practical augue. exercises Vivamus with ante ESA justo, software tempor et tools tincidunt BEAM, et, NEST, placerat and eget POLSARPRO lectus. Ut fringilla blandit ipsum, sit amet porttitor augue posuere ac. For further information including the daily programme see: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent ac quam id arcu tempor pellentesque. In hac The habitasse course was platea attended dictumst. by 70 In trainees: vel ipsum nisi. Proin et sapien ac Associate mi laoreet assistant condimentum professors vehicula vitae est. Aenean fringilla ante non ligula commodo ultricies. Curabitur accumsan tortor Senior scientists in eros dignissim id tincidunt lectus sodales. Duis congue sodales Post Docs. augue, ut hendrerit mauris condimentum sed. Nunc ac nisi Ph.D. augue. and M.Sc. Vivamus students ante justo, tempor et tincidunt et, placerat eget lectus. Ut fringilla blandit ipsum, sit amet porttitor augue posuere ac. 3. Signing of the Dragon 3 protocol agreement between ESA and MOST representatives on 5 June 0. Symposium participants at the opening plenary session 3. Dragon Final Results and Dragon 3 Kick Off Symposium participants, 5 to 9 June 0, Beijing 3 4. SAR practical class. Poster session at the Library of the Chinese Academy of Sciences 3. trainees being awarded certificates of attendance 4. 0 land training course participants, lecturers and organisers, MITL IECAS, Beijing, China 6 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 7

5 DRAGON 3 programme programme DRAGON 3 ESA Processing Tools ACADEMIC EXCHANGES Dr. Carsten Brockmann demonstrating BEAM (ocean training course, SKLEC ECNU, Shanghai in October 0) Software ESA is providing, free of charge and on the internet, a collection of user-friendly and open-source software tools for the visualisation and exploitation of EO data, from the Explorers, Envisat, ERS and Third Party Missions. Upgrades, documentation, example data sets and support are available on the related websites. Post graduate training Joint field visits Working within the framework of the Dragon cooperation, several European universities have made agreements with universities in P.R. China to train Chinese scientists at Ph.D. level A number of project teams have undertaken joint field campaigns in P.R. China to collect ground data and validate their results European and Chinese project team members joint field visit in China 0 NEST (Next ESA SAR Toolbox) has a viewer for visualisation and processing of ESA and 3rd party SAR data starting from Level. Users can develop new readers and processors for SAR data by using an Application Programming Interface (API). BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) is a collection of executable tools and an Application Programming Interface (API) for viewing and processing ESA & TPM data. It also has a plug in to read SMOS data. BEAT (Basic ERS & Envisat Atmospheric Toolbox) is a collection of tools and an application programming interface (API) for viewing and processing of ESA GOMOS, MIPAS, SCIAMACHY and GOME data. POLSARPRO The Polarimetric SAR Data Processing and Educational Tool is for processing multi-polarised SAR datasets including ALOS PALSAR, Envisat ASAR Alternating Polarisation mode products, RADARSAT- and TerraSAR-X. A wide-ranging tutorial and comprehensive documentation provide a grounding in polarimetry and polarimetric interferometry. BRAT (Basic Radar Altimetry Toolbox) is a collection of tools for processing, data editing, extraction of statistics and visualisation of results from radar altimetry data. It can read data from: ERS- & ; Topex/Poseidon; Geosat Follow-on; Jason-; Envisat; Cryosat-. GUT (GOCE User Toolbox) provides tools for the utilisation and analysis of GOCE Level products for applications in Geodesy, Oceanography and Solid Earth Physics. See the tutorial for information and guidance. GUT consists of a series of advanced processing routines. POLIMI Italy and Wuhan University, China The long standing cooperation will continue under Dragon 3 with a programme of post-doctoral research to be undertaken at POLIMI by a Chinese researcher under the supervision of professors Fabio Rocca and Daniele Perissin. Dr. Lu Zhang went from being a young scientist in Dragon- to being an Assoc. Prof. in Dragon-3. The research team will focus on undertaking and supervising young scientists research on the use of multi-frequency X, C and L-band SAR data for terrain motion studies and DEM generation with 6 test sites in China and in Antarctica. University of Twente, The Netherlands The research undertaken and ground networks developed under the previous Dragon programmes has been expanded into a wider and more detailed study now encompassing the Third Pole Environment (TPE) and investigating Essential Climate Variables (ECV s) in relation to regional water balance and climate change. An international team of European and Chinese researchers are engaged. There will be a continuation of calibration and validation activities using ground stations on the Tibetan Plateau and Himalayan areas with associated fields campaigns with a focus on calibration of a wide range of ECVs derived from ESA, TPM and Chinese EO satellite observations. POLSAR Group The cooperation between MITL IECAS and IETR University Rennes- started in 007 will continue in Dragon 3, with an active programme of seminars, student exchange, co-supervising of students and a research programme using both satellite and airborne Polarimetric SARs particularly with respect to forest height and biomass retrievals and new algorithms for POLSARPRO v5. In 03, IETR will host a visiting researcher from IECAS, and Ph.D. students from CAF and MITL IECAS respectively.. Project id. 0367: European and Chinese team members joint field visit, Almeria, Spain Nov. 0. Project id: 0609: European and Chinese project team, June 0 meeting 3. Project id. 0649: European and Chinese team members joint field visit, Gansu Province, China 0 Examples of global level products from ESA data, from left to right: Elevation & bathymetry; Sea Surface Temperature; Nitrogen Dioxide (NO ); Globcover; Earth s geoid 3 8 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 9

6 DRAGON 3 programme programme DRAGON 3 Dragon 3 cooperation management Dragon 3 Young Scientists & Oct. 0, meeting with Chinese University Hong Kong in preparation for the 03 advanced training course in ocean remote sensing 6 & 7 Oct. 0, ESA and NRSCC Progress Meeting with Chinese PIs, CAF, Beijing December 0, visit to the 03 Symposium venue, Sicily, Italy Supported post graduate training for young Europeans in geoscience applications Master of Science, doctoral degrees and research supervised by leading EO scientists in Europe and China Study periods, field work, and data collection in P.R. China Annual reporting at Dragon Symposia Piazza Borsa Conference centre in Palermo, Sicily Field visit by Forest Dragon team members and young scientists, NE China On and October 0, the organisation and planning for the 03 advanced training course in ocean remote sensing was initiated following a visit to Chinese University of Hong Kong (CUHK). This training course will take place from to 6 October 03. The course will be taught by leading Dragon cooperation scientists and will provide training in the exploitation of ESA s Explorers (GOCE, SMOS, Cryosat-) and the new Sentinels missions for ocean monitoring and science. On 6 and 7 October 0, ESA and NRSCC had a joint progress meeting with Chinese Dragon 3 lead investigators at CAF, in Beijing. ESA informed the PIs about the up-coming 03 Dragon Symposium. The status of the Dragon 3 projects since kick-off in June was reviewed and actions to facilitate access to EO data were made. In December 0, following a visit to Sicily, the venue for the 03 Symposium was finally selected in Palermo. The Symposium will bring together the joint Sino-European teams for reporting after year s activity. There will also be a poster session dedicated to reporting by young scientists. ESA has allocated resources to Dragon projects for training of young scientists. The applicable period is 03 to 06. ESA has placed contracts with the European Institutions participating in the Dragon projects. The types of training supported include: Doctor of Philosophy (Ph.D.), 3 years duration Post graduate Master of Science (M.Sc.), year duration for each year of the applicable period Post Doctoral Research (Post Doc.) 4 months with evidence of publication in leading scientific journal or conference proceedings ESA has requested the following deliverables to set up the contracts and monitor student progress: During 03 to 06, as part of their research, the young scientists will undertake study periods and field data collection campaigns in P.R. China. They will work with their Chinese counterparts. At the 03 Symposium in Europe, they will report on their project activities and their latest results.. Proposal to ESA for training support using a proposal template. Training Report to ESA (every 6 months) 3. Presentation of progress and results at annual Dragon Symposia 4. Any software developed as a result of the training support Meeting between ESA, NRSCC representatives and staff of CUHK, October 0 Participants of the Dragon 3 progress meeting, October 0, CAF, Beijing China POLSAR Chinese young scientists and their research professors, Dragon 3 KO Symposium, Beijing Chinese and European young scientists presenting research results at the 0 Dragon Symposium, Beijing 0 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE

7 DRAGON 3 programme programme DRAGON 3 ESA, TPM and Chinese EO Satellites and Instruments EO data TPM data For science and application development in China, ESA and NRSCC are providing EO data to the Dragon Sino-European teams. The instruments are used for monitoring the ocean land and atmosphere and the use of archive data allows for time series analysis and change detection since the 990 s to the present day situation. ESA has made agreement with other agencies for access to TPM data for science and application development. New SAR data China launched the HJ-C satellite (top) successfully on 9 November 0, which can provide continuity of S-band SAR observation, and form a constellation with HJ-A/B satellites to provide high-temporal data for disaster and environment monitoring. New optical data China will launch the BJ- satellite (middle) constellation on 04, which will be composed of 3 small satellites and load two kinds of payloads, PI and MSI, mainly serve for urban planning and natural resource monitoring. In addition, China successfully launched the ZY-- 0C satellite (bottom) in 0. ESA Current EO Satellites & Instruments Chinese Current EO Satellites & Instruments SMOS MIRAS Beijing- PI, MSI CRYOSAT- GOCE PROBA SIRAL EGG CHRIS CBERS-0/0 or ZY- HJ-A/B HJ-C WFI, CCD, IMSS CCD, HI, IMC SAR ESA Future EO Satellites & Instruments HaiYang- B COCTS, CZI ADM-Aeolus EarthCARE SWARM ALADIN ATLID, CPR, MSI, BBR EFI, ACLRMTR, GPS Rec et al. HaiYang- FY -D/C/3A/3B RA, SCAT, MWR Various on near polar(fy-d/3a/3b) Various on geostationary (FY-C) From 03 on, ESA will launch the next generation of EO satellites, the Sentinels (top), (centre) and 3 (bottom). Sentinel- will provide continuity of C-band SAR observations. Sentinels and 3 will provide land and ocean parameter retrievals from their suite of optical and thermal instruments. There will be a total of 6 satellites in the constellation, each Sentinel will have an A and B series, so ensuring high temporal re-visit and monitoring capability. Sentinels ESA & TPM Archive Missions ENVISAT ERS & ALOS ESA and TPM satellites and EO instruments A/B- SAR A/B - MSI 3 A/B - SLSTR, OLCI, SRAL, DORIS, & MWR ASAR, MERIS, (A)ATSR, RA-, MWR, GOMOS, SCIAMACHY, MIPAS ERS SAR, SCAT, ATSR, RA- ERS SAR, SCAT, ATSR, RA- Chinese Future EO Satellites & Instruments Beijing- CBERS-03/04 PI, MSI CCD, WFII TanSAT Measurement of CO CFOSAT SWIM, SCAT Chinese EO satellites and EO instruments China has launch in 0 the first of a constellation of S-band SAR satellites. There will be four satellites in the series providing high temporal re-visit capability for the China mainland and coastal areas. THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 3

8 DRAGON 3 programme programme DRAGON 3 ESA AND TPM EO DATA DELIVERY CHINESE EO DATA DELIVERY Sensor July 0 to April 03 ASAR (IM, AP) 3,9 MERIS FR 38 ERS SAR 039 ALOS 85 Total 574 Satellite June 03 to April 03 Beijing- 5 CBERS-/ 90 HJ 49 ZY-/3 3 FY 798 HY 68 Total 9975 Envisat ceased operations in April 03, since then, Dragon projects have been requesting archive data HJ--C SAR image of Beijing showing the Olympic Park stadiums ESA and TMP high bit rate data delivery (from July 0 to April 03) Chinese EO data delivery by Satellite Delivery of ESA and TPM high bit rate data on a project by project basis (from July 0 to April 03); NB: 9 more projects, not included above, have access also to Earth Explorers Missions (GOCE, SMOS, CRYOSAT) and atmospheric chemistry projects and low resolution products (ATSR, RA, SCISMACHY, MERIS Reduced Resolution) via FTP or HTTP Delivery of Chinese EO data by project 4 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 5

9 DRAGON 3 programme programme DRAGON 3 DRAGON 3 Study Areas Access to ESA, TPM and Chinese EO data Study areas include regions, lakes, cities and coastal zones all over China ESA & TPM EO data can be accessed using either on-line catalogues for low bit rate data or via the Earth Observation Link (EOLi) client for access to high resolution data. Users must first register to get an account. EO CAL / VAL on the Tibetan Plateau, 5 long term observation and measurement stations The wide swath width and high temporal revisit capability of the new constellation satellites such Beijing- and HJ--A/B, ensures high temporal frequency coverage over the Dragon 3 study areas and all of China. All of the projects study areas are accessible to view via the Dragon 3 website home page: HJ--C will ensure large area mapping applications in China s cloud covered southern regions whilst the SMOS satellite provides regional coverage of Sea Surface Salinity of China seas and estimates of soil moisture over most of China s land area.. To access low and medium resolution ESA & TPM data & products freely available on-line Access for registered users (with EO-SSO account) (A) Visit: (B) Click on login My Earthnet To register as a user, at the PI community website click on Registration and provide the requested information (if you do not already have an EO-SSO account) Once registered, repeat steps A & B above Access to Chinese EO data. Download by internet following registration A. FY and 3 satellite data, register at with NSMC: B. CBERS-0/0 and HJ-A/B /C satellite data, register with CRESDA for Chinese users at: C. HY- B & HY-, register NSOAS for Chinese & international users: Over China seas, ocean colour and thermal studies will be supported optical and thermal instruments on board ESA and Chinese by satellites, e.g. OLCI and SLSTR on Sentinel-3 and COCTS, CZI on board HY- A/B. The all-weather, day and night capability of microwave instruments such Sentinel- SAR and The acquisition of atmospheric instrument data such as from ESA s ADM instrument is global over China and will be used in support of CAL / VAL projects, along with ground based instruments.. Data ordering by NRSCC Provide data requests to NRSCC Dragon office for ordering the following Chinese EO data ( dragon_caf@63.com) Beijing- (and when operational) TanSAT CO mission (when operational). Restrained ESA & TPM data (via EOLI-SA using your dragon 3 PI account) Download EOLi at Login to EOLi Identify scenes required Place your order Study areas range in extent from major cities, Inland lakes, 3 Gorges Dam & regions 6 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 7

10 DRAGON 3 programme programme DRAGON 3 DRAGON 3 WEBSITE DRAGON UPCOMING EVENTS DRAGON 3 PROGRAMME INFORMATION & REPORTING PORTAL ADVANCED TRAINING COURSE IN OCEAN REMOTE SENSING News & Events Projects, partners & executive summaries Study areas Venue The course will be hosted by the Institute of Space and Earth Information Science, The Chinese University of Hong Kong (ISEIS, CUHK), P.R. China Symposia - programmes, abstracts and presentations Dates From to 6 October 03 ESA, TPM & Chinese EO satellites & instruments Brochures Registration: Registration is free of charge. The course is open to Chinese and SE Asian nationals Fok Ying Tung Remote Sensing Science Building, CUHK Advanced training courses Mid and final term proceedings Sponsors The course is being sponsored by MOST/NRSCC, Chinese University of Hong Kong and ESA Content: The lectures and practical sessions shall cover theory and processing of ESA, TPM and Chinese EO data for ocean monitoring and parameter retrievals over China Seas The training course programme and application form are available from the training course website: 03 Dragon 3 Symposium After year s activity since the formal programme KO in June 0, the joint teams will report on their latest results at the 03 Dragon Symposium. The Symposium will take place from 3 to 7 June 03 and will be held at the Piazza Borsa conference centre in Palermo, Sicily in Italy. The Symposium will be held over 3.5 days and there will be opportunities for team meetings during the week of the Symposium. Objectives st year reporting for all of the dragon teams Reporting by the young scientists engaged on the projects Team meetings and planning for the nd year s research activities Up-date from ESA and NRSCC and EO missions and SAR data gap filling 8 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 9

11 ID 0367 land & environment land & environment ID 050 Desertification monitor Dr. Gabriel del Barrio, Consejo Superior de Investigaciones Cientificas, Almeria, Spain Prof. Gao Zhihai, Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing, China Prof. Bao Yuhai, Inner Mongolia Key Laboratory of Remote Sensing and GIS, Hohhot, China Prof. Dimosthenis Asimakopoulos, Univ. of Athens, Greece Assoc. Prof. Constantinos Cartalis, Univ. of Athens, Greece Prof. Gong Huili, Capital Normal University, China Dr. Wang Mengjie, Beijing Normal University, China Study Areas The study areas considered involve Gonghe Grassland, Minqin Oasis-Desert, Otindag Sandy Land and three provinces in Mongolia, including the Tov aimag, Seleng aimag and Orhon aimag. Study Areas A high latitude continental city with a dynamic planning process and environmental challenges (Beijing) and a low latitude Mediterranean city with vulnerability in microclimatic changes (Athens). The objective is to develop a quantitative and operational technique system for desertification assessment in both China and Mongolia. The project will generate: () A classification approach for land use/ land cover; () Improved algorithms for sparse vegetation parameters inversion; (3) Determination of desertification benchmark; (4) The optimal indicators for desertification assessment through comparisons among vegetation fraction trend analysis, NPP scaling, RUE and its advance versions(drue); (5) An operational desertification assessment system at the national and regional scales; (6) Assessment of human contribution to desertification under climate change. Desertification land refers to the land productivity decline, land resources loss, etc., when the soil degrades to a certain degree, the soil nutrient content fell sharply. These changes of soil feature index are important to measure whether the soil is occurring desertification. As one of the important soil index, the accurate extraction and inversion for soil organic matter (SOM) is the key to the sandy land monitoring and evaluation. First of all, soil features parameters were analyzed, and their difference was distinguished between the sandy and unsandy land. Secondly, the correlation between SOM and reflectance in each band was analyzed, and the best inversion band combination was determined, then the quantitative inversion model of SOM was established and validated. Finally, the SOM was inversed quantitatively, and obtained the distribution of SOM in the Otindag Sandy Land. The results showed that the content of SOM diminished obviously in degradation land. The correlation between three bands of BJ- and SOM was relative well, and correlation coefficient (r) was as high as 0.7. But the inversion accuracy of multiple regression model for SOM was higher, and it was more stable than the linear regression model with single band, because it contained more information and reflected the divergence of different soil types effectively. The model was validated with independent sample, RMSE was 0.64 and accuracy was 6.65%. In addition, the vegetation fraction was estimated by decomposing the mixed pixel based on BJ- data, and the model was verified with ground measured data, R reach the point of Sustainable urban planning and management increasingly demands innovative concepts and techniques to obtain up-to-date and areawide information on the characteristics and development of the urban system. In this project, Earth Observation (EO) and urban modeling techniques are used to monitor changes in the urban environment by means of the definition and application of four, to a significant extent interrelated, urban indicators (UI) reflecting: ) urban fabric and sprawl ) urban microclimate 3) urban geological hazard 4) urban flood. Urban indicator. A time-series of EO data are used to map the dynamics of urban sprawl in the cities, based on the growth rates of the urbanized regions. The different urban land cover features are detected by implementing a machine learning classifier approach. The processing steps include: a) the geometric correction process, b) application of the brightness normalization method (Xu, 008) so as to handle difficulties in quantifying urban composition, c) shoreline extraction d) polygon masking so as to separate land surface from water bodies, e) application of a suite of machine-learning algorithms to extract urban features and f) production of thematic maps using 4 classes: background, water body, non-urban, urban. Urban indicator. Low and high resolution EO data are used for the estimation of Land Surface Temperature (LST) and subsequently the definition of Surface Urban Heat Island (SUHI) characteristics of the cities, such as development and spatial pattern (heat island or heat sink), growth and evolution (SUHI area in km), intensity (LST difference observed between downtown and the surrounding countryside). It should be mentioned that as a continuation of the DRAGON - project, low resolution data are used in conjuction with downscaling techniques (Stathopoulou and Cartalis, 009).. Field investigation, including spectral measurement, biomass calculation, etc. July-August, The classification of SOM and vegetation fraction estimation in Otindag Sandy Land based on BJ- image.. The study area, including sites both in China and Mongolia. Urban expansion in Athens - composite image for the period Urban expansion in Beijing The research about sandy information extraction is being performed by Dr. Junjun Wu, and the desertification evaluation and monitoring is being done by Dr. Hongyan Wang and Msc. Bin Sun. Young scientists (at postgraduate level) will be involved in urban modeling and image processing as well as in the assessment of EO data from various spectral bands (mostly visible and TIR) and temporal and spatial scales for urban applications. 3 0 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE

12 ID 055 land & environment land & environment ID 0650 Epidemics monitoring Land Subsidence Monitoring Dr. Yaxin BI, University of Ulster, UK Prof. LI Chuan-rong, Academy of Opto-Electronics (AOE), Chinese Academy of Science (CAS), China Dr. Michael EINEDER, Deutsches Zentrum für Luftund Raumfahrt,Germany Dr. ZHANG Yonghong, Chinese Academy of Surveying and Mapping,China Prof. Zhou Xiao-nong, National Institute of. Parasitic Diseases(NIPD), Chinese Center for Diseases Control and Prevention(CCDC), China, Dr. ZHANG Jixian, Chinese Academy of Surveying and Mapping, China Study Areas The study area is in the marshland of Junshan county, part of Yueyang, which is located in east Dongting Lake. Study Areas Luanchuan, located in the west of Henan Province, China, is a mineral rich area, which has been mined since the 970 s. The topography in this region is steep, with complex landforms and some unstable slopes. The project aims at fostering a collaboration of multi-discipline of remote sensing, intelligent data analysis and tropical disease research to extract environmental factors that are suitable for the incubation, growth and dispersion of vector-borne diseases (Schistosomiasis, Malaria, Dengue). The project focuses on developing spatial-temporal models to provide dynamical informative factors for studying vectorborne diseases and for real-time monitoring and early warning, thereby improving human health across the countries. Schistosomiasis is one of epidemic/infectious diseases, which severely affects human health. Previous studies have revealed that snails are an intermediate host for schistosomiasis breeding and transmission. By applying remote sensing innovative technology, a preliminary analysis on Landsat TM/ETM+, MODIS, ASTER DEM and ENVISAT ASAR imagery data has been conducted and several important environmental impact factors have been extracted. The project also looks at the correlation between environmental factors and transmission of the vector-borne diseases. Specifically, based on the fuzzy information theory, a quantitative model has been developed by using the field epidemiological data in 003, 005 and 007 and the associated environmental data including water, soil temperature and moisture, DEM, NDVI etc. which are extracted from satellite data. Taking input these factors derived from 009 satellite data, the model has been employed to predict the distribution and density of snails as illustrated (Fig. below). A comparative analysis has been also performed to validate the predicted results against the field survey data. The results demonstrate the promising of the developed model in predicting location and distribution of snails. This result lay down a pathway for carrying out the next phase of work for dynamically monitoring spatial-temporal distribution of vector-based diseases and early warning. Due to long-term mining activities, severe ground subsidence has taken place in Luanchuan mining area. This region suffered the most serious geological disasters in Henan province. By 0, geological disasters had occurred in 38 places. We used InSAR technique to map the ground deformation, and conducted a field campaign to investigate the affected area in May 0. There are no ground measurements of subsidence before. But we found clear evidence of subsidence at places where large deformation is detected by INSAR. To map the ground deformation, small baseline time-series InSAR technique was applied to a small ALOS PALSAR data stack which consists of 9 images acquired between May 008 and 7 Nov. 00. The L-band data was chosen because C-band data presents very low coherence in this mountainous vegetation-covered area. interferograms with perpendicular baselines less than 000m are generated high coherence point targets are selected from the average coherence image. By time-series InSAR analysis, ground deformation between May 008 and Nov. 00 in Luanchuan mining area is retrieved. Figure shows the accumulative deformation over coherent points during the two years. From this figure, we can see that the region from Chitudian to Shibapan has suffered the most serious deformation and the largest accumulative deformation reaches -84mm. The southwest mountain area is very stable. In order to analyze the temporal characteristics of ground deformation, 5 coherent points P~P5 are selected. The evolutions of accumulative deformation of the 5 points are given in Fig.. During the field investigation carried out in May 0, many evidences of ground subsidence were found, like wall cracks, ground subsidence in the backyard of nearby residences. Besides many underground mining activities, there are several big fields of open-pit mining (Fig. 3). In conclusion, ground subsidence caused by large scale mining activities has posed substantial threat to the safety of many buildings in this area. a TC_Wetness b LST c DEM (m) d Water e NDVI f Soil moisture g TC_B h density of snail. Environmental factors retrieved from RS data and predicted snail distribution and density of Accumulative deformation in Luanchuan mining area from 008 to 00. the temporal evolutions of estimated deformation on coherent points P~P5 3. Field investigation photo Zhaoyan LIU, Ph.D, research on a remote sensing monitoring model, and other two PhDs are also involved in satellite and field survey data analytics. WU Hongan, Ph.D, is with CASM. His research in this project is focused on SAR interferometry and its application to ground deformation monitoring.. Study area field work and ground instrumentation 3 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 3

13 ID 0666 land & environment land & environment ID 0666 Forest dragon 3 Category Dataset Prof. Christiane Schmullius, University of Jena, Germany c.schmullius@uni-jena.de Prof. Li Zengyuan, Institute of Forest Resource Information Technique, Chinese Academy of Forestry zy@caf.ac.cn Forest DRAGON Products ERS-/ GSV map 995 based on tandem coherence ASAR GSV map 005 based on BIOMASAR algorithm Field Campaign Data 0 Literature Databases Growing Stock Volume Data Forest Ecosystem Database Provincial Data Study Areas The Northeast and Southwest of China, Far-East of Russia and Continental Southeast Asia have been selected as study areas for the Forest Dragon-3 project. These areas stand for the major forest types of China and surrounding IIASA GSV map 005 Forest Canopy Height 005 To investigate scaling effects in forest ecosystem mapping with SAR data, to perform long-term analysis of forest GSV and forest structure over Northeast China based on SAR data, to link FOREST-DRAGON products with existing land use, land cover and/or fire products, to investigate synergy of optical and radar data for mapping forest ecosystems, to adapt current forest mapping algorithms to Eastern Russia, to adapt current and to develop new forest mapping algorithms for Continental Southeast Asia, to update the FOREST-DRAGON- forest map with Sentinel-/ data. Forest aboveground biomass (AGB) was estimated in Yunnan province of China by using forest inventory plots and optical remote sensing data. This result will be improved by fusion with radar data. Field work for forest parameter estimation has been undertaken in Northeast of China. The airborne Lidar data were acquired in the test site of Daxinganling in August and September of 0. The forest growing stock volume (GSV) map produced with ERS- / coherence images for and two G SV maps produced from Envisat ASAR ScanSAR data for 005 and 00 were intercompared with respect to several datasets (in situ, EO images and EO data products) to assess the plausibility of the GSV estimates, the contribution to land cover mapping and the dynamics over time. For this purpose, a multi-source database was set up including in situ data and EO data products. Land use / land cover (LULC) datasets identified mis-classification of GSV in the ERS dataset primarily for cropland. However, compared to the MODIS VCF product and a map of forest canopy heights, the GSV product captures the spatial variability of the forest landscape in a more reliable way. A forest above ground biomass (AGB) method was developed by using multi-sensor data. This framework uses field measurements to calibrate airborne and space borne LIDAR data. This will provide a spatial distributed forest biomass at LIDAR coverage scale. These estimated biomass from Lidar and field plots are in discrete pattern. Then this discrete biomass will be fused with remote sensing imagery data. To support validation activities or, at least, to verify the quality of the SAR-based GSV maps, a multi-source database was set up including in situ data, various open-source and intra-project EO-based (see table, right) datasets of GSV, vegetation cover and several forest and land parameters Estimated Forest AGB in Yunnan Province, China EO datasets, forest-related Land Cover Data Photo Libraries Other EO datasets MODIS NDVI (multiple years / multiple spatial resolutions) MODIS Active Fire (multiple years) ATSR World Fire Atlas (multiple years) MODIS Burned Area (multiple years) MODIS VCF Per cent Tree Cover (multiple years / multiple spatial resolutions) Pan arctic Vegetation Cover GLC000 MODIS Land Cover 005 Field Campaign Photos 0 Confluence Points Field Campaign Photos (University of Oklahoma) SRTM DEM 000 Landsat Surface Reflectance (multiple years) 4. Eastern Russia, Northeast China and 0 Validation Area. Continental Southeast Asia and Southwest of China 4. GSV change map from GSV estimates obtained with the BIOMASAR algorithm using Envisat ASAR ScanSARimages acquired in 005 and 00 (top), ground inventory plots and coverage by airborne LIDAR, NE China (left) Three young scientists from China will contribute: Dr. Guo Ying for biomass estimation algorithms development, Dr. Xu Guangcai for Lidar applications in forestry and Dr. Zhang Zhiyu for SAR application in forestry. At the University of Jena, one PhD student shall focus on multi-scale radar-optical synergies for forest monitoring, supported by MSc- and BSc-theses. 4 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 5

14 ID 0676 land & environment land & environment ID 0695 Forest modelling urban development & Climate Prof. Barbara Koch, Albert-Ludwigs University of Freiburg, Germany Prof. Zhang Xiaoli, Beijing Forestry University, China Prof. Yifang Ban, Royal Institute of Technology, Sweden Prof. Gong Peng, Tsinghua University, China Study Areas Sanming is a predominantly forested region located in the Northwest of FuJian province, People s Republic of China. The city has a warm and humid climate with high annual rainfall. Study Areas The study area include nine major urban clusters in China as well as selected Megacities around the world such as Beijing, Delhi, Dhaka, L.A., London, Mexico City, Sao Paulo and Shanghai. Investigation of new remote sensing technologies for forest ecosystem assessment in mountainous regions Development of models for the retrieval of forest parameters e.g. timber volume, biomass, non-timber products, forest biodiversity or woody biomass for energy using geo-data bases Models for assessment of a set of different ecosystem services Scenario development tools Multi-criteria decision support system Visualization tools and web-services We already have collected some data in the part of the research area including: satellite data from European and Chinese satellite platforms in single or multi-date forms, including optical multispectral (5 SPOT scenes, 4 ALOS and 0 ENVISAT ASAR scenes) in Beijing and Fujian. Other forms of geographical data such as reference forest inventory data from field surveys, socio-economic and climatic data. Pre-processing and preparation of the data including atmospheric and radiometric corrections, geometric corrections, haze/could removal and standardization of the optical data have been done. Envisat ASAR Radar data has started to be processed although still at the starting stage. We have finished the field data collection in districts and counties of Beijing forest sites, but field work needs to continue in Sanming site. We already have collected some data including inventory data, which are provided from other related project in study site. With the help of this data, we can conduct sampling with classification of different trees. In the aspect of LIDAR data, 0 sample plots have been collected for ground-based three-dimensional (3D) laser scanning. Point cloud data is processing including data matching between different instrument sites in one plot, trees modelling, noise removal etc. The overall objective of this research is to investigate multi-temporal, multi-scale, multisensor satellite data for analysis of urbanization and climate impact in China and around the world for sustainable urban development. The specific objectives are: to develop new algorithms that can rapidly detect urban clusters and land cover changes; to monitor urban clusters/urban agglomerations in China and globally using multitemporal multisensor data; to evaluate high resolution spaceborne SAR and optical data for mapping spatial configuration of selected urban landscapes; to assess what impact urban land cover change has on the environment and climate. The KTH-SEG using an Edge Aware Region Growing and Merging algorithm (EARGM) have been developed and applied to ENVISAT ASAR and HJ-B Data for urban land cover mapping. The results show that the performance of KTH-SEG is superior than ecognition in defining urban segments and linear features as shown in above Fig. top row (Ban and Jacob, 03). Fast and efficient methods have also been developed for global urban extent extraction using ASAR Wide Swath Mode data. The preliminary results show that the methods are very good in mapping high-density urban classes as shown in above Fig. (Gamba and Lisini, 03). TerraSAR-X data were evaluated for monitoring of megacities using a pixel-based classification algorithm to delineate urbanized areas from other land cover types. The algorithm uses the original intensity SAR data in combination with texture information followed by an automated, threshold-based image analysis procedure. The classification algorithm detects high values in areas with a comparatively high texture measure caused by vertical manmade structures, as shown in Fig. (Taubenböck et al., 0). Change detection methods are being developed for monitoring urbanization using multitemporal SAR data. A non-local means despeckle algorithm and Markov random field are being adapted to improve urban change detection using multitemporal SAR data and the results are promising. Location of Sanming city, SE China (left), ALOS AVNIR- image (above) and field data collection in 0 (below) From Beijing Forest University: Li Mingyan, Ph.D,, Tree height parameters extraction based on SAR data. Wang Shuhan, Ph.D, TGB (Tridimensional Green Biomass) Measurements using Individual Tree Crown Model Based on High-Res. EO data. WangKun, MSc, AGB estimation combining spectral responses and textures using Quick Bird Imagery. Four PhD students and five MSc students will be trained through this project.. TerraSAR-X image over Shanghai;. Top: Urban Land Cover Classification in Beijing using KTH-SEG; lower: Urban Expansion Mapping in Beijing (left), Shanghai (center) and Pearl River Delta (right) 6 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 7

15 ID 0448 renewable resources renewable resources ID 0549 Farmland Drought forest change monitoring Dr. Stefano Pignatti, National Research Council of ItalyInstitute of Environmental Analysis Italy Prof. Dr. Raffaele Casa, University of Tuscia, Italy Dr. Yang Guijun, Beijing Research Center for Information Technology in Agriculture, China - guijun@nercita.org.cn Prof. Dr. Wang Jihua, Beijing Research Center for Information Technology in Agriculture, China - jihua@nercita.org.cn Prof.Dr. Huang Wenjiang, eijing Research Center for Information Technology in Agriculture, China - yellowstar068@63.com Mike Wooding, Remote Sensing Applications Consultants Ltd., UK mikew@rsacl.co.uk Dr. Tan Bingxiang, Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing, China tan@caf.ac.cn Study Areas We choose Shaanxi Province (typical arid and semi-arid region), Inner Mongolia Province(plateau farmland) and Beijing as our study areas from different scale and landscape. Study Areas Test sites are located in Yunnan and Guangxi Provinces China, and in Thailand, Laos, Cambodia and Vietnam. Most of these sites measure 80 km x 80 km. Our objective is to monitor farmland drought more effectively through developing new methods and new techniques based on multi-source remote sensing data and wireless sensor network data. It will include: ) Improve the ability of monitoring key vegetation parameter and soil moisture in farmland drought based on multi-source data; )Construct more effective quantitative drought diagnosis index system. A new soil moisture estimation technique over vegetated fields was developed based on spectral feature space method and linear decomposition of mixture pixel method. The traditional VIs methods monitored drought status effectively only when the vegetation was greatly affected by water stress, and was not suitable for low vegetated areas. So in our method soil Red and NIR band reflectance was obtained from mixed pixel through removing the effect of vegetation. The soil line equation was combined with a developed empirical relationship between the vegetation canopy and mixed pixel reflectance in the Red- NIR spectral feature space. Then, remote sensing image with in-situ data in Beijing, China, was used to establish the retrieval relationship of soil reflectance to soil moisture. Finally, the method was evaluated and validated by HJ- CCD and Landsat TM5/ETM+ data, and it was concluded that the result was reasonable (Fig. ). Soil components at field scale were estimated from hyperspectral CHRIS PROBA data. Soil samples were collected in two fields from 0-30cm layer. Soil components including clay, silt and sand were analyzed in the Lab. A PLSR model was developed and the CHRIS and in-situ data was used for calibration and validation (Fig. ). The project will develop the use of Sentinel- SAR data for monitoring tropical and sub-tropical forests. The emphasis is on the early detection of logging activities to control resource use as part of sustainable development. Another aspect is the quantification of afforestation, an important process which is happening in China to counter the carbon emissions caused by deforestation and other human activities. Initial work has concentrated on developing a processing chain using SAR data for detecting forest change. This includes data acquisition, pre-processing, change detection, post-processing and validation/ follow-up. Adaptive change detection algorithms have been developed and fine-tuned in work carried out within the framework of the EU FP7 REDD FLAME project. Baseline forest maps have been produced using optical satellite data for 5 test sites, including Sayabouiy Province, Laos. With the failure of Envisat it has not been possible yet to use newly acquired SAR images to start monitoring changes within the forested areas at the test sites. However, there is interest in using RadarSat, TerraSar-X or Cosmos Skymed data. Once Sentinel- data are available the project will focus on the use of strip map dual polarisation 5m x 5m data. At least image every months per test site will be used to monitor forest change and provide this information to the relevant authorities. The detected changes will be automatically post-processed in a GIS environment for reduction of false alarms, removal of noise and exclusion of non-forest land cover types. During the post-processing, additional information about the nature of the changes will be added, depending on the availability of auxiliary datasets and/or local expert knowledge. From the final change locations, an up- -to-date alarm map will be made.. Service concept and processing chain for early detection of deforestation (n.b. developed in the REDD FLAME EU FP7 project) 8 young scientists, including 5 Ph. D., Ph.D. students, and MSc, participate in the recent research. 3 young scientists attended the advanced land remote sensing training held by DRAGON 3 in 0.. Soil clay percentage estimation by CHRIS data. Result of soil moisture inversion in Beijing Feng Qi, Ph.D. Student and Fan Yinglong, Masters Student, Institute of Forest Resources Information Technique, Chinese Academy of Forestry. Xichao Dong of University of Sheffield/Beijing Institute of Technology will undertake a Ph.D. developing SAR change detection techniques.. Baseline forest map for test site in Sayabouiy Province, Laos. (From Landsat TM) 8 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 9

16 ID 0605 renewable resources renewable resources ID 0640 crop production estimation crops monitoring Prof. Lieven Bydekerke, Flemish Institute for Technological Research, Belgium Prof. Wu Bingfang, Institute of Remote Sensing Applications, Chinese Academy of Sciences, China Prof. Pierre Defourny, Universite Catholique de Louvain, Belgium Dr. Fan Jinlong, National Satellite Meteorological Center China, China Study Areas It arranged 3 study areas for crop production estimation in China and Europe totally. Two study areas located in Yucheng and Hengshui of China, and the third one located in the junction area between Belgium and France. Study Areas In this project, two sites will be selected, one in North China plain and another in Europe covering the Northern France and Belgium. Each site is covering about 300 km by 300 km area. For the Chinese site, the centre Latitude This project is to improve the efficiency and accuracy of methods on crop identification and crop yield estimation with Sino-European satellite data. It includes independent method to discriminate different crop combinations without ground survey using multi-source images, crop acreage estimation integrating multi-resolution satellite data and crop modelling using microwave data and crop growth models. Crop classification is a key issue for agricultural monitoring using remote sensing techniques (Jia et al., 0). Object-oriented image classification method is used to identify different crops. Multi temporal HJ- CCD images were acquired during May 0 th to October 5 th, 0. Based on the difference in phenology of different crops, training samples were selected and used to analyze distinctive features of different crop type of different objects. The key distinctive features were used to discriminate different crops. Validation results showed that the classification accuracy was 0.9 with Kappa coefficient of Time-series of MODIS vegetation index (VI) products with 50 m resolution data (October 0 th, 0 to May 0 th, 0) were acquired for crop mapping in the United States. Winter wheat had unique curve shape of time-series VI data which can be used for classification. A shape-matching approach was applied to the pre-processed timeseries VI data to extract winter wheat planting region. Winter wheat acreage of U.S. was also estimated by zonal statistic of pixel numbers at province level. The statistical results were also compared to the reports released by USDA and the variation of winter wheat acreage was close to that of USDA reports. This proposal is to get better crop parameter retrieval by taking advantages of both ENVISAT-MERIS and FY-MERSI and then to enhance the application of satellite data in the crop assessment by the means of the assimilation of the crop parameters retrieved from satellite data into the crop model. FY is the acronym of Fengyun satellite in Chinese or Wind and Cloud in English. FY-3 is the second generation of China s polar-orbiting meteorological satellite. Based on the experience and technology of previous FY- series satellite, it has made a great step forward. The FY-3 satellites can obtain global, all-weather, three-dimensional, quantitative and multi-spectral parameters of atmosphere, land surface and sea surface. The first experimental satellite (FY-3A) was launched on May 7, 008 and the second experimental satellite (FY- 3B) on November 5, 00. The FY-3 series satellites will work for 5 years. Agricultural monitoring is one of key application areas. FY-3A and FY-3B both carry payloads of which VIRR and MERSI are both key valuable sensors for the agriculture monitoring. They have the similar observing capabilities as the NOAA/AVHRR and EOS/MODIS or ENVISAT/MERIS. With the support of Fengyun Satellite Program, the web based Fengyun Meteorological Satellite data services system was developed in the past years. Since 005, the domestic and foreign users have been able to access to the Fengyun Satellite data at The left figure above shows the web portal of the Fengyun satellite data services. The web portal provides the users with real-time and historical satellite data that were acquired and are acquiring from the FY-D, FY-3A, FY-3B, FY-D, FY-E, NOAA-5, NOAA-6, NOAA-7, NOAA-8, EOS/TERRA, EOS/AQUA, MTSAT-, MSG- and so on. The operator of the FengYun Satellite, NSMC is operationally providing vegetation growth monitoring in China. The right figure above showcases vegetation growth condition monitoring with FY-3/ MERSI.. Field campaigns were carried out in 0. Crop identification in Hongxing Farm (A) and crop mapping in the United States (B) in 0 A There are young colleagues with PhD. Degree and 4 young graduate students in this project. The young scientists will be involved in the field campaigns and methods development of surface variables and crop production estimation with remote sensing data. In Chinese team, a master student is working on the FY-3 satellite data processing and assimilating satellite data into the crop model for the crop monitoring. In European team, a Ph.D student is working on the satellite data processing and information retrieving. B Web portal of the FY satellite data services (above) and the vegetation growth condition monitoring with FY-3/ MERSI (left) 30 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 3

17 ID 0667 renewable resources renewable resources ID 0668 forest resources research LU Change & Water Quality Dr. Mika Karjalainen, Finnish Geodetic Institute, Finland, Prof. Juha Hyyppä, Finnish Geodetic Institute, Finland Prof. Huang Guoman, Chinese Academy of Surveying and Mapping, China Dr. Zhao Zheng, Chinese Academy of Surveying and Mapping, China Prof. Apostolos Sarris, Laboratory of Geophysical - Satellite Remote Sensing and Archaeo-Environment Foundation for Research and Technology, Hellas, Greece asaris@ret.forthnet.gr Prof. Cheng Tao, University College London, United Kingdom Tao.cheng@ucl.ac.uk Prof. Gong Jianhua, Institute of Remote Sensing Applications, Chinese Academy of Sciences, China jhgong@irsa.ac.cn Prof. Qigen Liu, Shanghai Ocean University, China qgliu@shou.edu.cn Study Areas We study forest resources mapping techniques in Finland and China by coupling SAR satellite data with information derived from ALS, airborne X/P-band SAR, and profiling scatterometer. Study Areas Areas of Xin an River watershed and Qiandao Lake (9º N. 8º E) as well as wetlands and aquatic ecosystems on Chongming Island (3º N. 9º 8 4 E) The objective of the project is to develop efficient canopy measuring techniques based on satellite-derived elevation and surface models, including the following detailed aims: () to extract canopy height models from laser derived elevation model subtracted from satellite given DSM (Sentinel- C-band interferomertry and stereoradargrammetry), () to extract canopy height models from Chinese airborne SAR equipped with X and P bands (CASMSAR), and(3) to compare canopy height models with field data and UAV-based radar profiler. Airborne Laser Scanning has revolutionized the mapping and monitoring applications of forest resources due to its capability to extract accurate 3D information of forest structures. See the figure above (left). The data was collected using the FGI s Mobile Mapping System. We believe that the potential of satellite SAR data in forest mapping is present as well on the extraction of spatially accurate 3D information. Recently, we published the first results of the plotlevel forest resources prediction based on the stereo-radargrammetric approach using the German TerraSAR-X satellite data. Our aim is to test the proposed approach using Sentinel- SAR data also, as soon as Sentinel- data will be available. The CASMSAR system (X/P band SAR) has been and will be tested in the interferometric and radargrammetric processing in order to create accurate 3D models of the forested areas. The FGI has also started the development of the radar profiler to be mounted on a UAV platform, which can be used to measure vertical profiles of forest canopy in the Finnish study area. The radar profiler data will be compared with data obtained from Airborne Laser Scanning and SAR satellite data. The 4 main objectives are: (i) understanding the impacts of watershed processes on fisheries and natural ecosystems through multidisciplinary study (ii) demonstrate the importance of remote sensing for accurate monitoring ecological processes and natural resources, (iii) Produce pollution risk maps for water and fishing resources, (iv) develop methods and algorithms for dynamic generation and update of various thematic maps to support for any development project in the area study areas. Accurate land use/land cover information from 979 to 009 was extracted from Landsat MSS, TM, ETM and ALOS using object-oriented classification. Ground surface temperature (GST) maps were also generated from LANDSAT images. Vegetation, land use/land cover dynamics and the mutual conversions among different land use/ land cover classes were analyzed through the calculation of change matrix derived from classified images and application of land use/ land cover transfer model. There was great absolute changes in the vegetation cover and its associated dynamicity owing to the internal conversion to main land use classes at different time intervals. The decrease in vegetation cover was significant and the ratio of vegetation cover area to the total area of the island had decreased from 7% (979) to 5% (009) while Chongming Island expanded by 358km from 979 to 009. The results indicate an increase in water area, buildup area and road during the same time period. The change trend of land use/land cover closely related to the requirement of social and economic development besides natural evolution factor. The results suggest that change trajectory of land use/land cover can provide a good quantitative measurement for a better understanding of the spatio-temporal pattern of land cover change and can provide much of the information needed for decision-making. GST from LANDSAT (7/07/009) (ground validated) & changes in land use over a 0 year period Campaigns for GPS & UAV data acquisitions (photo) production of vector data in GIS system (below) Several students from both FGI and CASM have participated in the study and are working with interferometric SAR data, developing radar profiler system and preparing their Ph.D. studies. Point cloud representing forested area (left) and X-P airborne SAR composite of forest test-site in China (right) tree crowns show high backscatter at P-band There are 5 young scientists in the team, who will be trained at Master and PhD levels in ecology, remote sensing, fisheries, and data mining/visualization. 3 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 33

18 ID 0350 HAZARDS HAZARDS ID 0557 Forest Fires & Emissions Wetlands Monitoring Prof. José-Luis Casanova, Head of the Remote Sensing Laboratory, University of Valladolid, Spain Dr. Qin Xianlin, Institute of Forest Resources Information Techniques, Chinese Academy of Forestry Dr. Hervé Yesou, Service Régional de Traitement d Image et de Télédétection, Strasbourg University herve.yesou@unistra.fr Pr. Huang Shifeng, Institute of Water Resource and Hydropower Research, China huangsf@iwhr.com Study Areas The Northeast and Southwest of China have been selected as the study areas. They stand for different forest regions of China. At the same time, there always take place forest fires in these two regions every year. Study Areas Even Nen river wetlands (Heilongjiang Pr) are historical AOI, focus will be on the Yangtze reaches, from upstream, Napahai and Rouergai areas, to downstream, Poyang and Dongting lakes as well as Anhui lakes. To describe the forest fire FRP probability distribution, on several areas of China, as a power law To obtain the forest fire FRE from the FRP distribution power law To get the biomass burned on the analyzed areas by means the FRE To define a relationship between the FRE released and the CO/CO forest fire emissions To analyze the post-fire environment in terms of the emitted FRE Field works for the fire information collection and Fire Radiative Power (FRP) estimation have been studied in Southwest of China. At the same time, the burned area mapping method has been developed by using HJ-A/B CCD and IRS images. The method for estimating the FRP has also been developed by using HJB IRS and FY3A VIRR images. Simultaneously a complete analysis of fire statistics on China has been carried out by means of fires detected by MODIS-Terra and MODIS Aqua sensors, from 000 to 0. The aim of this study has been to fit the fire distribution to a power distribution law. This analysis has been applied to different areas: first all China, and after that, we have split the territory into three areas: Northeast, Southeast and the rest of China. The obtained results allow to compare and characterize the different types of fires and to classify them. Also, it is possible to estimate the number of fires do not detected by the MODIS sensor and to estimate the power law parameters for each region. The results will be reported in the symposium of year 03. Facing challenges such as, water resources, environmental preservation, and public health, the project s goals, having water monitoring as the corner stone are: - large and small inland water bodies monitoring, in terms of extent, height and quality), associated with water resource or storage capacities analysis (from drought to flood); 3 NRT mapping action, 3- wetland ecosystem understanding, 4- epidemiology, as diseases are closely related with water resource dynamic 5- regional interaction and global context. On the continuity of DRAGON and, the monitoring of Poyang Lake was maintained on a relative high frequency thanks to the access of a large set of HJ completed by CSK and TerraSAR SAR images. An analysis of the water surface of Poyang Lake, was done, highlighting the periods with excess or deficit of water. A major fact is the apparent increase of drought event, both in summer following the rain period, and during dry season. Preliminary results obtained over Wuchang lake, Anhui Pr, based on a long term monitoring, based on EO archived combining with water level and quality and aquaculture information indicate that since 973, Zizania had been expanding rapidly in Lower Lake, Zizania expansion happened mostly between two flood events. Over the Napahai basin (Yunnan Pr), analysis of HR and imagery appear to show two important trends in wetland distribution since 980. Until the mid-990 s there appears to be an increase in permanent water bodies and surrounding wetland areas due to the construction of water reservoirs. From the mid-990 s until 00, wetland areas declined, correlating to an expansion of urban areas, agriculture and impervious surfaces (e.g. large roads, airport). These patterns mirror trends in Black-necked Crane counts in the basin showing increases from the 980 s through the late 990 s and declines since 000. Field experiments for Fire Radiative Power measurement and obtained results of its time evolution in Southwest of China (upper row) and Spain (bottom row) Three students have taken part in the project from China and Europe for their master degree. Mr. Xi Zhu is working on burned mapping using HJ-A/B CCD. Mr. Jing Liao is working on FRP estimation using FY 3A/B images and Mr Rodriguez is already concluding his PhD. Particular attention is paid on the integration of young scientist in this DRAGON project. Two PhD s students are directly involved on wetland habitat quality and quantity for migrating wildfowl, plus one on water quality. A few MSc are also involved. Shengjin Lake (Anhui Pr) viewed by HJ- on the 3rd of May 00 (4km) (left top) Poyang Lake water surface variations between 000 and 0, Blue water excess, red water deficit Black-necked Cranes feeding (photo) 34 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 35

19 ID 0606 HAZARDS HAZARDS ID 0607 Landslides Monitoring Crustal Deformation & Infrastructures Prof. Thomas Glade, Department of Geography and Regional Research, Austria Dr. Zbigniew Perski,Carpathian Branch, Polish Geological Institute - National Research Institute (PGI-NRI), Poland zbigniew.perski@pgi.gov.pl Dr. Liu Guang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China liug@ceode.ac.cn Dr. Bai Shibiao, Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, China shibiaobai@njnu.edu.cn Prof. Ramon Hanssen, Delft University of Technology, Netherlands R.F.Hanssen@tudelft.nl Dr. Zhenhong Li, University of Glasgow, UK Zhenhong.Li@glasgow.ac.uk Prof. Zhang Jingfa, Institute of Crustal Dynamics, China Earthquake Administration, China zhangjingfa@hotmail.com Prof. Xu Cajun, Wuhan University, China cjxu@whu.edu.cn Study Areas Carpathian Landslides in Poland, Landslides in Wenchuan earthquake area (LongMen Mountain in Sichuan province), Longnan area in South-East of Gansu province, Three Georges landslides Hubei province Study Areas Long-term and seasonal deformation of the Qinghai-Tibet Railway (QTR) in the Yangbajing Basin and its surrounding area, Tibet; The 4th March 0 MW 6.8 earthquake occuring in Eastern Burma (Myanmar). The proposed project aims to carry out an extensive exploitation of available remote sensing data and methods to evaluate their importance for landslide hazard, risk management and disaster prevention. This includes the detailed documentation of landslides occurrences for the past decades and the analysis of current movement trends which are required to predict future conditions. For mapping of landslides and evaluation of their activity, SAR data and SAR interferometry will be extensively used. In China, a preliminary study on landslide susceptibility was carried out to investigate the dynamic changes induced by the 008 Wenchuan earthquake. It was found that both rainfall-triggered and seismic-triggered landslide susceptibility maps for the Zhouqu area in the Bailongjiang Basin were fully consistent in 57.9%. Only one level of change was observed in 36.% of the study area and 6% of the study area showed more than one level change. This research utilised ERS-/, Envisat data and third-party data of digital elevation models, ortho-photos, geological and land-use maps, precipitation records and information on peak ground acceleration. For Carpathian region in Poland, SAR Interfeometric processing on various areas have been performed. The analysis included ERS-/, Envisat data and third-party data of TerraSAR-X (see figure above). The results were compared with Polish Geological Survey ground data. The analysis of TerraSAR-X data for Roznow lake vicinity reveals recent landslides activity e.g. Klodne, June 00. However, analysis of the ERS SAR archive has shown no landslide activity prior to this catastrophic event. Further investigations will be made using ASAR and PALSAR archive data. The Qinghai-Tibet Railway (QTR) crossing multiple active faults is prone to earthquakes and other natural hazards, and hence monitoring deformation is crucial to secure the QTR and its passengers lives. On 4th March 0, a MW 6.8 earthquake struck Eastern Burma (Myanmar), which was the largest shallow event in this region in the past 50 years; determinaton of its source parameters will improve our understanding of future earthquake hazards in this region. The QTR has been in service since July 006, and 6 scenes of Envisat images collected during the period between April 006 and May 00 were analysed to investigate the long-term and seasonal deformation of the QTR in the Yangbajing Basin and its surrounding area, Tibet. Fig. shows the mean deformation velocity along the railway; it is clear in Fig. that the QTR roadbed was relatively stable in the Yangbajing basin but it exhibited obvious subsidence in mountain areas most likely due to permafrost. Two tracks of ALOS PALSAR images were used to investigate the focal mechanism and slip distribution of the 4th March 0, MW 6.8 Burma earthquake (Feng et al., 03). Three different SAR techniques, namely conventional interferometry, SAR pixel offsets and Multiple Aperture InSAR (MAI), were employed to obtain the coseismic surface deformation fields along the ~30 km length of the fault rupture. Our optimal model suggests that the rupture occurred on a near-vertical sinistral strike-slip fault with a strike of 70 degrees, and slip occurred mainly in the upper 0 km with a maximum slip of 4. m at a depth of.5 km (Figure ). Envisat ASAR SBAS + PSI displacement map, Miedzybrodzie Poland (right); Gansu, China, Spatial distribution of landslides (centre) & susceptibility map (above) 3 Ph.D. students and 6 Master students will undertake terrain motion studies using field-based research coupled with EO and ground based InSAR, multi-spectral & LIDAR data and GPS. Quantitative risk assessment, vulnerability analysis and landslide mechanisms will be determined using spatial modelling with different input variables. European side: one 3rd-year PhD working on QTR in Delft and one nd-year PhD on earthquakes in Glasgow. Chinese side: one RA, two PhD and one MSc on InSAR algorithms in CEA; one lecturer and three PhD on earthquakes in Wuhan. Two PhD students, Yongsheng Li (CEA), Peng Li and Qiong Li (Wuhan) visited Glasgow in 0.. The 0 Burma earthquake: (a) coseismic interferogram from ALOS Track 6; (b) coseismic interferogram from ALOS Track 486; (c) slip distribution from InSAR; and (d) the grey area indicates the sum of scalar moment released along strike whilst the blue line shows the normalized slip as the function of the depth. Note: Inset in (b) shows the location of the earthquake.. Mean LOS deformation velocity of the QTR in the Yangbajing Basin and its surrounding area during the period from April 006 to May THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 37

20 ID 0665 HAZARDS HAZARDS ID 067 Terrain Motion & Landslides Case Studies Seismic Anomalies Detection Prof. Jan-Peter Muller, Mullard Space Science Laboratory, University College London, UK Prof. Wolfgang Neimeier, TU- Braunschweig, Germany Prof. Zeng Qiming, Peking University, China Prof. He Xiufeng, Hohai University, China Dr. Yaxin Bi, University of Ulster Prof. Terry Anderson, University of Ulster Prof. Zhao Guoze, Institute of Geology, China Earthquake Administration Prof. Zhan Yan, Institute of Geology, China Earthquake Administration Study Areas Landslides occuring in the Three Gorges region in China, particularly the 60 km stretch between Badong and Zigui; subsidence in South China, particularly around the Pearl River Delta. Study Areas On the basis of the coverage of the CSELF ground based network, and SWARM and CSES satellites, our research is focused on the areas of Beijing Capital lithosphere, Northeast China and Yunnan province. The Three Gorges region suffers from widespread and varied landslide hazards. Along with the practical importance of identifying and monitoring unstable and inhabited slopes, the densely vegetated region provides a useful test for the application of frequently acquired m resolution Synthetic Aperture Radar (SAR) images to understand the mechanisms of slow-moving landslides beyond urban areas. Additionally, the characteristics of landslide deformation can be a major limitation in InSAR analyses which assume a smooth spatial gradient of deformation. This study compares different techniques (conventional SAR Interferometry, pixel-offset and pixel-offset time-series) using SAR images from two satellite platforms (ESA Envisat and DLR TerraSAR-X). Coherence analysis suggests there is a strong seasonal component along with rapid temporal decorrelation associated with vegetation growth and frequent rainfall. Despite this, the highest resolution TerraSAR-X m Spotlight data allows the active landslide boundaries to be accurately mapped (Fig. ). Through subsequent image pairs a basic time-series of movement can be generated using conventional InSAR techniques. The accumulated deformation over 5 months of data is consistent with pixel-offset maps generated from a single pair of images as well as a least squared inversion of many pixel-offset maps. This comparison of techniques helps validate our measurements without ground data, and the consistency between InSAR and pixeloffset techniques provides confidence in interpreting the horizontal displacement measurements obtainable from the -dimensional offset maps (Fig. ). Finally, the time-series of landslide movement appears to be related to the draw-down of the Three Gorges reservoir. This can also help with interpretations of the failure mechanism that hypothesize that the movement is a reactivated ancient landslide deposit, triggered by the increased ground water level following the reservoir s completion. The overarching goal of the project is to develop viable methods and techniques for detecting anomalies from electromagnetic data observed by SWARM, CSES and the CSELF ground-based network in addition to characterizing anomalies. Our research is then focused on investigating correlation between characterized electromagnetic anomalies, and how these anomalies are related to seismic activities. The project also plans to employ precursors derived from thermal infrared data observed by CBERS-0&0 and Sentinel-3 satellites to complement electromagnetic anomalies. Detecting electromagnetic anomalies from satellite and terrestrial sources is challenging. The effective methods and meaningful results could be regarded as a cornerstone for scientists to predict seismic events []. In the past months, the project partners has focused their research on development of intelligent data pre-processing methods and precursor extraction methods, and employed the methods to study two earthquakes occurred in Taoyuan, Yunan province and in Wenchan, Shichuan province. Fig. shows a preliminary analysis result on the Taoyuan earthquake, which were derived from the transformation of magnetic and electric field data from time domain to frequency domain, finally presented in time domain. Fig. shows the corresponding wavelet coefficients, which reveal changes reflected in each of frequency bands. Fig. shows one year maxima lines of NOAA outgoing longwave radiation (OLR) used for a cross validation to the analysis results of the Taoyuan earthquake as presented in Fig.. These results are not only compatible with the latest study on OLR observed in the upper ionosphere in [], they also provide a very good basis for the improvement and continuous development of our methods and techniques [] and application to SWARM and other satellite data in next phase of research work. European side: one st-year PhD in UCL and one 3rd-year PhD in Glasgow working on landslides. Chinese side: Two PhDs in Peking University, one RA, two PhD and one MSc in CEA working on InSAR algorithms. Yongsheng Li (CEA) and Zhang Jingfa (CEA) spent 6 months in Glasgow in day interferogram of TerraSAR-X Spotlight data for the Shuping landslide. Indicates approx..6 cm of movement in the radar LOS direction. The horizontal offset (almost perfectly in the direction towards the river) between the first and last available TerraSAR-X Spotlight images Two PhDs and two MSc have been involved in space and terrestrial data acquisition, software development and data analysis. Two young scientists have also participated in the result interpretation.. Data transformation and change detection (left), with corresponding wavelet coefficients (right). Maxima lines of NOAA data 38 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 39

21 ID 0686 HAZARDS HAZARDS ID 0686 Seismology Dr. Cécile Lasserre, Institut des Sciences de le Terre, Grenoble, France Dr. Marie-Pierre Doin, Institut des Sciences de le Terre, Grenoble, France Dr. Sun Jianbao, Institute of Geology, China Earthquake Administration, China Prof. Shen Zhengkang, Peking University, China Study Areas Tibetan plateau: major active faults and large lakes. Earthquake prone areas in China and surrounding regions. The objective of this project is to provide precise measurements of surface displacement in China, and combine complementary techniques (high resolution optical images, GPS and InSAR) to better constrain models of lithosphere response to stress and strain variations throughout the seismic cycle, or non-tectonic sources, such as stress loading from lake level changes. We aim at detecting and analyzing lateral variations of coupling during the interseismic stress buildup period along selected faults, that may influence future earthquake patterns and the regional stress field, up to large distances from the fault. InSAR and GPS data will be merged into regional strain maps and models of the present-day elastic/ anelastic deformation. To document coseismic slip distribution, its relation with fault segmentation and its evolution from one earthquake to another, we will also use high-resolution optical imagery to map offsets of morphological markers. We will also extract non tectonic signals from InSAR time series, and focus on the monitoring and modeling of vertical deformation induced by water level fluctuations of large lakes in Tibet. This is critical for properly correcting, thus interpreting GPS and InSAR-derived small displacement rates. It also brings information on the lithosphere rheology, that in turn can be used to model fault behavior during the seismic cycle. Haiyuan fault. Fault geometry is among the key parameters that controls the initiation, propagation and arrest of seismic events. Earthquakes have been observed to nucleate or terminate near geometrical irregularities along faults. The coseismic slip distribution is also influenced by the fault segmentation, with fault intersegments usually associated with local slip minima. Such minima should be compensated over the entire seismic cycle, possibly by co- and postseismic deformation or by creep in the seismogenic zone at different stages of the seismic cycle. We have extended our study to the west and focused on a selected fault jog along the central Haiyuan fault : the 5 km-length Tianzhu pull-apart basin, south of the 97 Gulang earthquake zone. We have analyzed GPS data and tested a time series analysis of all archived InSAR data in this mountainous area, based on our NSBAS small baseline approach, with atmospheric phase delays correction from Global Atmospheric Models (ERA Interim) and DEM error correction, before unwrapping. It helps improving coherence and unwrapping capability of interferograms and allows us to provide a preliminary velocity map of the present-day strain distribution across the central Haiyuan fault, consistent in overall with GPS data (Fig.3). Large earthquake deformation. One of the important objective of this project is to monitor large earthquake deformation in China and surrounding regions, and provides fast and reliable solution for these earthquakes. The accumulated knowledge about the large earthquake models is a basis for understanding earthquake physics, and will lead us to better earthquake preparedness. Unfortunately, there is no more data for obtaining the earthquake deformation currently, due to the ends of Envisat and ALOS satellite missions. We show here a large earthquake deformation in Myanmar in 00 (Fig. ), which was captured by ALOS satellite just few months before the end of its life. Both ascending and descending data were acquired in short term, and the coherence of the interfermetric pairs is quite good for a thorough analysis. An efficient algorithm is also developed for the purpose of the earthquake deformation inversion. North China subsidence. North China is one of the well-known earthquake prone areas. However, we have limited information about the deformation process of the ground. We even know little about the earthquake fault motion in a long interseismic period, though the historic earthquakes are not rare. Hence, we intend to extract detailed surface deformation by using InSAR time series, to discriminate different source of ground motion in this area, and better understand the seismic hazard of this populous region of China (Fig.3).. ALOS InSAR deformation of the March 4, 00 Myanmar Mw 6.8 earthquake. Accumulated InSAR deformation across North China region from ALOS Interferometry 3 3. GPS velocities and InSAR average Line Of Sight velocity map across central Haiyuan fault (track 04), with profiles of elevation, LOS and fault-parallel GPS velocities (from S. Daout) We requested funding for a PhD in France to work on the mechanical modelling of geodetic data (InSAR, GPS) in northeastern Tibet. One post-doc in Caltech is collaborating on the project (InSAR processing and modelling). We expect at least one MSc student coadvised by french and chinese participants. 40 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 4

22 ID 0649 hydrology hydrology ID 0664 Water Cycle & River Basins Yangtze River Basin Hydrology Prof. Harry Vereecken, Julich Research Centre 3, Germany Prof. Li Xin, Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences, China Prof. Marco Mancini, Politecnico di Milano, Italy Prof. Li Jiren, China Institute of Water Resources and Hydropower Research, China Study Areas The Heihe River Basin (HRB) in China characterizes distinct cold and arid landscapes. The Rur Catchment in Germany is heavily instrumented and also a validation/calibration site for the ESA SMOS mission. Study Areas The test area is the Upper Yangtze River basin in China closed at the three gorges dam with a total area of about sqkm. The main river length is equal to 400 km with an average discharge of 34 m 3 s - Water budget components, including precipitation, evapotranspiration (ET), soil moisture (SM), snow water equivalent, runoff, and groundwater storage will be estimated using multi-source remote sensing observations. Corresponding time series products of these variables will be generated. These products, in combination with hydrology and land surface modeling, will be integrated by data assimilation methods to precisely close the land water budget at the river basin scale. With support of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER, Li et al., 03), field observations have been successfully initialized and measurement campaigns have been conducted in the central stream of the HRB in summer 0. A flux observing matrix (Fig. ) composed of eddy covariance (EC) and large aperture scintillometer (LAS) in addition to a densely distributed ecohydrological wireless sensor network have been established to capture multi-scale heterogeneities of ET, leaf area index (LAI), SM and soil temperature. With this setup complex problems are addressed, e.g. heterogeneity, scaling, uncertainty, and eventually to fulfil the purpose of closing the water cycle at the watershed scale. Airborne missions have been flown with the payloads of imaging spectrometer, LiDAR, thermal imager and microwave radiometer. Satellite images were preprocessed, such as PROBA-CHRIS (Fig. ), ASTER and TerraSAR-X. Simultaneous ground measurements have been conducted over specific sampling plots and transects to obtain validation data sets. So far, data processing and analysis is conducted. Some preliminary results have been achieved, in particular, new retrieval methods and algorithms have been developed with respect to albedo, ET, land surface temperature, radiation and vegetation. The main objective of the project is to assess water quantity and quality for the Yangtze River basin to support politics of sustainable development. The water quantity analysis is assed with two hydrological models (the fully distributed FEST-EWB and the semi-distributed Xinanjiang (XAJ), coupled with different satellite images and ground data. Both models analyze the behaviour of the main components of water balance for the whole basin area considering also hydropower and agriculture water use computing flow discharge and soil water content. The water quality analysis will be focused on the detection of suspended solid sediments, turbidity and chlorophyll in the three gorges dam reservoir and along the river network from satellite data and eventually ground data. A georeferenced data base for the entire basin has been implemented for meteorological data, satellite images and ground data. Satellite images have been selected and analysed: AATSR and MODIS for land surface temperature (LST) estimate, MERIS for snow cover area computation and land use definition, ASTER data for the Digital elevation model. The hydrological model FEST-EWB parameters have been calibrated for the upper Yangtze River basin, based on the comparison of simulated and observed satellite land surface temperature and river discharge. Flow duration curves at main river cross sections, pixel wise evapotranspiration and water availability maps have been estimates (Corbari et al. (03)). XAJ model parameters have been calibrated against observed discharge in a typical basin within the research area. Low relative errors on volume and high Nash Sutcliffe index value of daily discharge have been found, confirming XAJ model goodness. Around 0 PhD and MSc students are actively involved in our project, contribute to the studies mentioned above, and provide more insights into the topics related to the development and validation of terrestrial remote sensing products.. PROBA-CHRIS image acquired over Zhangye oasis on June, 0. Flux observation matrix composed of EC and LAS instruments Corbari, C., Mancini, M., Li, J. and Su, Z. (03). Can satellite land surface temperature data be used similarly to ground discharge measurements for distributed hydrological model calibration?. Hydrol. Res. Submitted.. Comparison between observed and simulated volumes from XAJ model in one typical basin in the Upper Yangtze river basin. Comparison between AATSR land surface temperature and simulated RET from FEST-EWB in the Upper Yangtze river basin 4 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 43

23 ID 0680 hydrology hydrology ID 0697 Hydrology Products Dongting Lake Flood Dynamics Prof. dr. Massimo Menenti, TU Delft, The Netherlands Dr. Li Jia, Wageningen University and Research Centre, The Netherlands Prof. Dr. Liu Qinhuo, Institute of Remote Sensing Applications Chinese Academy of Sciences, China Prof. Dr. Shi Jiancheng, Institute of Remote Sensing Applications Chinese Academy of Sciences, China Dr. Claudia Kuenzer, German Aerospace Center, Oberpfaffenhofen, Germany, Prof. Dr. Li Jing, Beijing Normal University, Beijing, China Study Areas The ground and airborne validation campaigns are carried out in the HeiHe River Basin (NW China). The data products cover entire China. Study Areas Dongting Lake extending from North and East is China s second largest inland water body, located in the centre of the Yangtze River Watershed. To produce data products on 0 land surface biophysical variables using multi-source data over Qinghai Tibet Plateau and related River Basins, including: Leaf Area Index, Fractional Vegetation Cover, Fraction of Absorbed Photosynthetic Radiation, Land Surface Temperature, Albedo, Evapotranspiration, Soil Moisture, Snow Cover, Snow Water Equivalent. To validate the data products using ground and airborne data collected during the HIWATER Experiment in the HeiHe River Basin. The hourly LST and LSE were estimated from FY-C/E satellite data for the Tibetan Plateau region, with a spatial resolution of 5 km x 5 km and for the period from January st 008 through December 3st 00, and gap-filled with the Multi-channel Singular Spectrum Analysis (M-SSA).Shortwave and longwave computations (Fig.), namely net solar and net longwave radiation, were performed for clear sky as well as for cloudy conditions. In the computation of solar radiation, a digital elevation model and broadband atmospheric radiative transmittance factors are used in order to account for the topographic and atmospheric transmissivity effects. Time series of turbulent heat fluxes were produced on a daily basis. A demonstrator was produced for the whole year 008 : 366 simulation are performed for the local solar noon ( pm Beijing Time). Results of simulations were evaluated against ground flux measurements for the grids corresponding respectively to the Nagqu, Namco and Linzhi stations. This study showed that estimated daily ET derived using the gapfilled LST compared well with ground measurements, with a RMSE of 0.49mm, while the results using the non-filled LST only show acceptable performance, with RMSE of 0.65mm when percentage of cloud covered time is limited to 30%. The study also demonstrated that under moderate cloud covered hours, e.g. lower than 30%, over a day there is no need to fill the gaps in the LST time series. However, in a few cases when cloudy condition is dominant over a day (cloud covered moments larger than 80%), the estimated daily ET using the gap-filled LST tends to be overestimated. A major threat to the Dongting Lake s ecosystem that is home to about 7 million people is severe water pollution through urbanisation, industry, and agricultural intensification. Objective of this project is the assessment of Dongting Lake dynamics using remote sensing techniques the assessment of flood pulse dynamics, wetland development, and the quantification of wetland degradation, as well as the derivation of land use changes in the direct lake s surrounding. As a first step the derivation of the extent of surface water inundation is considered a prerequisite for further analyses of lake dynamics and wetland habitat dynamics. In Dongting Lake area the existence and development of the wetlands heavily depend on the fast changing water coverage. The derivation of surface water can be conducted using radar or optical remote sensing data. Depending on the time span the data is available analyses can go back to the 970s (with Landsat) or back to 990s (with ERS). For radar based derivation we use an automated approach that is a combination of thresholds and morphological operations to mask the area of low backscatter indicating water. In-depth analysis of ASAR time series data for the past decade regarding inundation length and frequency are ongoing. Further remote sensing based analyses will comprise the derivation of land cover changes in wetlands as well as land use changes in agricultural areas utilizing optical remote sensing data from different sensors. Example of radiative balance components computed for April th 009 Extreme Inundation Dynamics in East Dongting Lake area within half year; a-nd of July 00;b-th November 00;c-9th of December 00 There are ten PhD students contributing to the project focused on the retrieval of fpar, LAI, phenology, ET, soil and foliage component temperatures. Two projects focus on time series analysis of sateliite data. One project deals with glacier mass balance and glacial lakes. Exchange of young scientists: Chinese Assoc. Prof. and Post Doc will visit DLR in the second half of 03 to undertake Earth observation based analyses. The German PI also contributed to the ESA DRAGON lectures in October in Beijing. 44 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 45

24 ID 053 CAL/VAL CAL/VAL ID 06 Atmospheric Dynamics from LIDAR SMOS CAL VAL & Soil moisture Dr. Oliver Reitebuch, German Aerospace Center, Germany Prof. Jan-Peter Muller, Mullard Space Science Laboratory, University College London, UK Prof. Liu Zhishen, Ocean University of China, China Dr. Yang Lei, China Meteorological Administration, China Dr. Yann Kerr, Center for the Study of the Biosphere from Space, France Dr. Rogier van der Velde, Uni. of Twente, The Netherlands Prof. Zhang Weiguo, National Space Science Center,Chinese Academy of Sciences, China Prof. Wen Jun, CAREERI/CAS, China Study Areas Wind lidar observations from ADM-Aeolus validation, study of atmospheric dynamics, surface reflectance, and assimilation experiments using a numerical model Study Areas Study areas include the Taklamakhan desert and the Tibetan Plateau. Both hold instrumentation for Cal/Val of SMOS and SAR-based soil moisture products. Taklamakhan is also suitable for Cal/Val of SMOS TB. The global observation of atmospheric wind profiles remains to be of highest priority for weather forecasting. ESA decided to implement the Atmospheric Dynamics Mission ADM-Aeolus to demonstrate the potential of the wind lidar technology for global wind profiling. ADM- Aeolus will carry the first wind lidar in space. This project supports ADM-Aeolus mainly in validation of the data products, besides studying atmospheric dynamics, including assimilation experiments, and the properties of the surface reflectance. A direct-detection wind lidar (an ADM-Aeolus prototype called AD) and a coherent-detection -µm wind lidar were deployed on the DLR Falcon aircraft from 007 to 009 for pre-launch validation campaigns (Fig. ). More than 00 findings relevant for the satellite instrument on-ground testing, calibration, validation and processing algorithms were derived. A more fundamental issue related to the spectral line shape from Rayleigh-Brillouin scattering was addressed by an experiment from a mountain observatory. For the first time the Rayleigh-Brillouin line shape was resolved in the atmosphere by horizontal lidar observations (Witschas et al. 0). A mobile Doppler lidar for three-dimensional wind measurements developed by Ocean University of China OUC and operated by China Meteorological Administration CMA is suitable for the ground-based validation during the overpass of ADM-Aeolus. The lidar provided wind profile and sea surface wind measurements for various activities, including the Olympic Sailing Games in 008 (Liu et al. 008). A field campaign was carried out to evaluate the measurement accuracy of the lidar (Fig. ). The intercomparison measurements with radiosonde showed that the lidar is able to provide accurate wind profile measurements for the future validation of ADM-Aeolus. A global soil moisture product is available from ESA s Soil Moisture and Ocean Salinity (SMOS, Kerr et al. 0) mission and the launch of Sentinel- in 03 facilitates the development of a Synthetic Aperture Radar (SAR)-based product. The algorithms used for the soil moisture estimation are subject to various sources of uncertainty. The objective of this project is to quantify and remedy these uncertainties (Radio Frequency Interference, Vegetation/Surface Roughness Effects) via Cal/Val with ground measurements. A database with the available ASAR WS scenes and SMOS observations has been assembled with matching in-situ soil moisture/temperature measurements. The in-situ measurements have been quality-checked and processed towards volumetric soil moisture, m3 m-3, using laboratory determined site specific calibration coefficients. All (+500) ASAR Wide Swath mode data sets have been reprocessed and for the Tibetan Ngari prefecture soil moisture estimation has been performed using the algorithms of the Italian Institute for Applied Physics (IFAC-CNR) and ITC-UT algorithms. Initial analysis yielded promising results. The comparison with in-situ measurements generated for both algorithms correlations on the order of 0.65 and 0.80 for the VV and HH polarization, respectively. The ITC-UT algorithm produced, however, a significant bias, which will be further investigated. Yann Kerr participated in the Commission for Space Research (COSPAR) Capacity Building workshop in Beijng. Additional visits are planned for 03, including a field campaign in collaboration with the Cold and Arid Regions Environmental and Engineering Research Institute of the Chinese Academy of Sciences (CAREERI/CAS).. Field campaign in the Taklamakhan Desert (top) and Tibetan Plateau (bottom) PhD students and Post-Docs are involved in the study team, especially for the development of algorithms related to the analysis of a direct-detection wind lidar at DLR and OUC. It is planned to start an activity at UCL for deriving cloud heights and winds.. Photo of the direct-detection wind lidar AD and the coherentdetection -µm wind lidar inside the DLR Falcon aircraft.. Photo of the mobile wind lidar from OUC and CMA at the Beijing observatory in summer 0 during a radiosonde launch Four PhD students and one postdoc have visited Europe and have been working on Cal/Val of products and algorithm development. Rogier van der Velde received the excellent young foreign scientist fellowship from the National Science Foundation of China (NSFC).. Soil moisture maps derived from ASAR WS images (Van der Velde et al. 0) 46 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 47

25 ID 0609 POLSAR & INSAR POLSAR & INSAR ID 0569 POLSAR Terrain Measurement Prof. Eric Pottier, University of Rennes, France Prof. Chen Erxue, Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, PR. China Prof. Fabio Rocca, Politecnico di Milano, Italy Prof. Li Deren, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing Wuhan University, China Study Areas China test sites: Yigen (LULC classification), Genhe (forest parameter inversion), Mianzhu (building height CS-Pol- TomoSAR algorithm); and the ESA BIOSAR, AGRISAR European campaign test sites (methodology) Study Areas Our research is mainly focused on two study areas. One is the Shanghai metropolitan area in East China. The other is the Three Gorges Reservoir area (from Badong to Zigui along Yangtze River) in Central China. Strengthen the established fruitful collaborations between European and Chinese partners in Pol-InSAR and quantitative forest resources information extraction science and contribute to the radar remote sensing technique development for local-to-global environmental monitoring of the terrestrial covers through developing general and original PolSAR and Pol-InSAR analysis methodology of LULC and forest parameters extraction with the introduction of new scientific concepts; investigating time-series analysis and the impact of different polarization mode; developing PolSARpro Software and training of young scientists. We have registered to the ESA Airborne Campaign data and got the BioSAR 007, 008 and 00 data; In the Northeast of China, two test sites (Yigen and Genhe) were established, the Airborne data of LiDAR and CCD were acquired with Radasat- PolSAR data, TerraSAR-X dual-polarization data and ground true data, such as LULC ground visit point record, forest plot survey, forest sampling line survey, and surface roughness and moisture measurement. Corner reflectors were installed in the Yigen test site, the radiometric and geometric calibration data were collected for the calibration of Radarasat- and TerraSAR-X SAR data. Three L-band Airborne PolSAR data sets over the Mianzhu test site were also collected. Some research topics carried out are as follows: analysis of the impacts of topography for forest height estimation (Li W., etal., 0), supervised classification based on Fisher Linear Discriminant[], azimuth stationarity extraction method based on Rician Distribution, and one compact-pol calibration algorithm for a wide-band GBSAR system were developed and validated[]; Compressive sensing based PolTomoSAR building and forest 3D structure parameter inversion methods were developed with better result; We also contributed to the Advanced Training Course in Land Remote Sensing 0 with three lecturers and IECAS as the key host. The primary objectives of this project include the developments of advanced SAR interferometry techniques and their applications in topographic mapping and measuring Earth surface deformations (ground subsidence and landslide, etc.). Satellite SAR data acquired by ESA missions (ERS-/and ENVISAT ASAR), Chinese mission (HJ- C) and TPM (ALOS PALSAR) will be used as major test data. Highresolution SAR data from German TerraSAR-X and Italian COSMO- SkyMed will also be used when available. Effective monitoring of ground subsidence is an important task for disaster prevention and mitigation. In Dragon &, time series of archived ERS-/ and ASAR data were used separately to retrieve ground subsidence within the downtown area of Shanghai. In Dragon 3, we pay more attention on the subsidence in the coastal area of Shanghai, particularly the deformation on seawalls. First results with PALSAR and ASAR data are shown in Fig.. Validation using levelling benchmarks shows that subsidence measurement accuracies achieved by the two data stacks are 4.8 mm and 4.6 mm respectively. (Pei et al., 0) Thousands of landslides are distributed across the Three Gorges Reservoir area. During Dragon and, we identified landslide movements within Badong County. In Dragon 3, we cross-validatethe results from multiple data stacks covering the same area. One PALSAR stack from an ascending orbit and two ASAR stacks from one descending and one ascending orbit respectively are used. Results of landslide deformation measurement are shown in Fig.. Cross-validation is performed after unification of the three results by projection of the LOS displacement vectors onto the down-slope gradient direction. The root-mean-squared difference of deformation velocity between the two ASAR stacks at common PS points is 7.0 mm/a and that between the ASAR ascending stack and the PALSAR stack is 8.6 mm/a. (Liao et al., 0). Subsidence on seawall at southeast Shanghai measured by L-band PALSAR and C-band ASAR data Three to five PhD students are to be exchanged between China and ESA partners for three to six months co-research work.. Airborne ground truth data collection team, Yigen test site (top); linear forest transect sampling (bottom). (a) L-band PolSAR image; (b) Classification legend; (c) L-band classification result using Fisher linear discriminant; (d) L/C dual bands result using Wishart classifier; (e) L/C dual bands result using Fisher linear discriminant. Five PhD students and three Master students participate in our research in Dragon 3. They are involved in data processing and result analysis. They also take part in field surveys at Shanghai.. Landslide deformations in Badong observed from multiple ASAR and PALSAR data stacks 48 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 49

26 ID 059 geodesy geodesy ID 0677 Geoid & GOCE GSM4GCM Dr. Kirco Arsov, Finnish Geodetic Institute (FGI), Finland Prof. Wen Hanjiang, Chinese Academy of Surveying and Mappin, China Prof. Dr. Nico Sneeuw, University of Stuttgart, Germany Prof. Dr. Li Jiancheng, School of Geodesy and Geomatics, Wuhan University, China Study Areas GOCE mission validation in Chinese and Finnish territory. Main study area is satellite geodesy in general and determination of the Earth s gravity field in particular. Study Areas Our satellite altimetry and gravimetry applications focus on river catchments and coastal seas relevant to China: Tibet plateau, Yangtze catchment, Yellow river upper reach, South- and East-China Seas, Northwest Pacific Our main objective is to see how the new GOCE mission models perform in China and Finland. To assess this we choose some very challenging test areas in China and Finland with high terrain variations. Our other objective is to compute one geoid for Finland and part of China by including the new GOCE models. Optimal GOCE combination with already available long wavelength models such as CHAMP or GRACE based is foreseen. The satellite altimeter data from ERS-/, GEOSAT, and T/P are used to derive the marine gravity anomalies. We used EGM008 model during the remove-restore processing. The inverse Vening-Meinesz formula is used, and the resolution of gravity anomalies is. Results are shown in Fig.. We will use these anomalies for GOCE models assessment over China. We will also use some land gravity data in China in combination with this set of anomalies. In Fig. the available Nordic gravity data is shown. We are currently screening, improving, and transforming this data before using it with GOCE models. Together with this, a compilation of one new and updated Nordic Digital Elevation Model (DEM) is ongoing. It will be used further in the data reduction. We will perform new data reduction with updated DEM (0x0m resolution). Then GOCE models will be assessed with respect to this data. Different DEM data reduction methods are currently under assessment, such as prism integration, Gaussian quadrature formula as well as polyhedras. Extensive activities in seismic and other geophysical data acquisition are currently ongoing. This includes compilation of new high resolution Moho-map for Finland and compilation of a new Finnish density map to be used in gravity reductions. The project aims for a combined and consistent analysis of the satellite gravimetry and satellite altimetry (ENVISAT, HY-, TPM). It will take advantage of the new multi-satellite SWARM mission to bridge the gap of gravity field monitoring between the current GRACE mission and its successor, at least on the longest scales. Moreover support will be provided to the constellation design of a future Chinese gravimetric satellite mission. The continued monitoring of global change phenomena is a critical issue of great societal interest. Satellite based techniques are particularly useful for such purposes, as they allow a systematic data collection on a global scale and in a synoptic and repeatable way, Fig.. In this project we make use of satellite missions such as:. GOCE, dedicated to measuring the Earth s gravity field with unprecedented accuracy and spatial resolution to advance our knowledge of ocean circulation;. SWARM, a geomagnetometry mission, consisting of a constellation of three satellites, used here as a GRACE-bridging mission; 3. ENVISAT, carrying a.o. a radar altimeter; and 4. HY-, a new marine remote sensing satellite equipped with an altimeter. A further innovative aspect of this project is the consistent combination of geometry and gravity in space and time in one reference system for the integrated analysis of the changing Earth. As an example, the figure at the right displays the change in sea surface topography as determined by multi-satellite altimetry and the GOCE-GRACE combination model GOCO03S. The results reflect how the Kuroshio Current changed its width and velocity in the central area between 993 and 009. Also a change in blending characteristics towards the end of the Kuroshio is revealed, Fig.. In the course of the project one PhD student from FGI and three PhD students from CASM will be actively participating in the project.. Altimetry derived gravity anomalies in China. Nordic gravity database Between the European and Chinese groups several young scientists participate. At least 0 PhD students perform research in the areas of spaceborne gravimetry, precise orbit analysis and satellite altimetry. They are supported by a similar number of PostDocs and (Associate) Professors.. Long-term mass trend from GRACE in terms of equivalent water height. Monthly velocity change of Kuroshio between 993 and 009 by combining satellite altimetry and GOCO03S geoid 50 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 5

27 ID 030 cryosphere cryosphere ID 050 Himalayan Glacier Dynamics Sea Ice Monitoring Dr. Noel Gourmelen, Institut de Physique du Globe de Strasbourg, France Dr. Tobias Bolch, TU Dresden, Germany Dr. Urs Wegmuller, GAMMA RS, Switzerland Prof. Cheng Xiao, Beijing Normal University, China Prof. Liu Shiyin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, China Dr. Wolfgang Dierking, Alfred Wegener Institute for Polar and Marine Research, Germany Dr. Markku Simila, Finnish Meteorological Institute, Finland Dr. Xi Zhang, First Institute of Oceanography, State Oceanic Administration, China Dr. Yonggang Ji, First Institute of Oceanography, State Oceanic Administration, China Study Areas We study glaciers from the entire Himalayan chain including the Himalaya, Karakoram, Hindu Kush, Pamir and Tien Shan sub-regions. Study Areas The Bohai Sea and its coastal regions are significant economic areas in China. Sea ice poses a great threat to coastal construction and manufacturing industry, leading to severe economic loss to China. To quantify secular and seasonal changes in the mass and stability of the Himalayan glaciers. Specific objectives are: Observation of glacier area changes and debris-cover evolution Quantifying glacier elevation and elevation changes Quantifying secular and seasonal variations in the flow, and flow divergence, of the glaciers of the HTTSK region as well as their uncertainties. Assimilation of Earth Observation (i.e. Dh/Dt and Dv/Dt) with field observations for estimates of glacier mass balance. Glaciers velocity We have collected SAR and Multispectral datasets over the study areas (ERS, ENVISAT, ALOS-, Landsat) and submitted proposal for access to proprietary datasets and new missions (TerraSAR-X, ALOS-). Glacier velocity has been obtained over some study areas for period covering the ERS tandem phase (996, 998 and 999) [fig. ], which will serve to analyse temporal variation in glacier velocity. We are involved in a Swiss-Sino project to monitor a glacier lake in western China. The Kyagar glacier sometimes interrupts the Yarkant river and generates a glacier lake. From this monitoring we have access to TerraSAR-X scenes that we used now to also monitor the flow of the Kyagar glacier. A technical report on glacier velocity mapping strategy was compiled, to get feedback and to iterate it with him and develop cooperation within the team. Glaciers height from CryoSat- (CS) CryoSat- Lb and L products over the study area show mixed results. Due to the complex surface topography the CS tracking process often fails to see the surface of the valley glaciers, tracking is instead done at the height of the mountain peaks. Some glacier valleys are imaged and waveforms show significant backscatter energy and coherent heights. Further work is undereway to validate these measurements [fig. ]. The objectives of this project will develop further the techniques implemented as part of the Dragon- programme (ID: 590), extend automatic or semi-automatic ice characteristics extraction methods using multi-sensor satellite data, and improve techniques for monitoring of sea ice based on optical and SAR images. The technology developed in the project can be used in the operational sea ice monitoring of the Bohai Sea and other ice-covered areas. Each remote sensing sensor has its own advantage and disadvantage. SAR is capable of sensing the dielectric properties and surface and volume structures of sea ice. Optical sensors measure the spectral characteristics of sea ice in the visible and near-infrared range. For this reason, fusion of cloudless optical and SAR images can enhance the differences between sea ice types and provide complementary information for sea ice classification. An image fusion approach of SAR and multispectral data is proposed. ENVISAT SAR and CBERS multispectral data are used for the experiment. The results demonstrate that spectral and SAR texture information are preserved on a sea ice area, and the fusion result is effective for sea ice interpretation and classification. In the project the focus is also on sea ice thickness retrieval using both SAR and as most recent work hyperspectral data. From field measurements in the Bohai Sea the reflectances of ice of different thickness were obtained. Based on the in-situ data, we developed a sea ice thickness retrieval model for hyperspectral data and tested the model by using airborne hyperspectral data to retrieve sea ice thickness. The results show that ice thickness in the Bohai Sea varied from.0cm~30.0cm. Amaury Dehecq, PhD student at IPGS since 0/0, evaluation of CryoSat- for retrieval of Himalayan glacier topography and quantification of temporal glacier dynamics. Ms. Meijie Liu, Ph.D. student, dealing with sea ice detection by SAR data. Mr. Qinchuan Xie, postgraduate, focussing on image fusion using multi-sensor data. Ms. Qing Wu, postgraduate, working on sea ice detection by multi-sensor data.. Glacier flow from Interferometric analysis of ERS tandem phase data from the year 999. One color cycle represents 5 m of motion. Height change (m) fromcryosat- and SRTM over Karakorum ASAR image, IMP mode (5m resolution), :54 (Beijing Time) (left), CBERS image, 9.5 m resolution, :00 (Beijing Time) (centre), Fusion image showing classification of ice types (right): open water - green; level ice - pink; ice shearing area - purple and crimson; deformed ice - white. 5 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 53

28 ID 06 cryosphere cryosphere ID 0674 Glaciers & Hydroligical Dynamics Cryosphere Dynamics - Tibetan Plateau Prof. Ralf Ludwig, Department of Geography, Ludwig-Maximilian-University Munich, Germany r.ludwig@lmu.de Prof. Ke Changqing, School of Geographic & Oceanographic Sciences, Nanjing University, China kecq@nju.edu.cn Dr. Michael Eineder, Remote Sensing Technology Institute, German Aerospace Center. michael.eineder@dlr.de Dr. Andy Hooper, Department of Geoscience and Remote Sensing, Delft University of Technology, Netherlands. a.j.hooper@ tudelft.nl Prof. Lin Hui, Institute of Space and Earth Information Science, The Chinese University of Hong Kong. huilin@cuhk.edu.hk Prof. Jiang Liming, Institute of Geodesy and Geophysics, Chinese Academy of Science. jlm@asch.whigg.ac.cn Study Areas The Yigong Zangbo and Parlung Zangbo catchments are located in the southeast of Tibetan Plateau. There are many glaciers in the two catchments, and they are main glacier distribution areas in the Tibetan Plateau Study Areas Nam Co (Co means lake in Tibetan) Lake, once the largest lake in Tibet, locates at 9E3N. Bam Co is adjacent to the north Nam Co. The Puruogangri ice field is located in the northern centre of the Tibetan Plateau. To use Earth Observation to support the needs for the monitoring, mapping, and water resource potential evaluating of glacier change in the Yigong Zangbo and Parlung Zangbo catchments. Some key glacier variables will be retrieved in virtue of ESA and other satellite data. Finally the applicability of quantitative remote sensing in cryosphere applications and glacier water resource potential evaluation will be promoted. Available SAR image pairs have been used to successfully estimate surface velocity pattern in the YZB. Herein, we examine the potential of SRFT method for flow estimation of the glaciers. These features confirm some basic lateral patterns in motion of valley glaciers and help us to explain better the glaciers movement. On both sides of the glaciers are the mountains, which limit the movement of the glaciers towards a definite direction. Immediately near the sidewalls of the valley glaciers, the ice is almost motionless due to the lateral and bottom frictions. Away from the sidewalls the flow speed increases to a maximum around the main flow lines. The glacier-surface velocity distributions throughout the YZB show strong spatial variations with elevation changes. Along the glacier main flow lines, there are multiple local maxima or minima, indicating that the velocity of the glacier flow down valley is not constant. Among the glaciers, mean velocity is between 5.8 and 05.5 m/a, and the Glacier No.5a has a maximal velocity of 4.58 m/a. Because all of them are oceanic-type glacier, generally the velocities are larger than those of continental-type glacier (Huang and Sun, 98). Our project aimed at cryosphere dynamic in the Tibetan Plateau by synergistic applying both microwave and optical earth observation associate with ground measurements. By regarding glacier, permafrost and plateau lakes as an integrated system, we seek to analyse its relationship and reaction with global climate change. Water balance between each sub system and its affection to engineering structure would also be analysed, and then integrate with Virtual Geographic Environment (VGE). Two adjacent lakes, Bam Co and Nam Co, increased by 3.74% (Fig. ) to km and 3.57% to km in 990s and 000s, respectively. The latter also presented m water level increasing detected by both Envisat/RA and Icesat altimeter. By deriving water loading caused ground subsidence using StaMPS/MTI (Hooper, 007) with ERS&Envisat SAR data, we concluded a mm/year rate surround Bam Co (Fig. ), while no significant subsidence found for Nam Co adjacent area. By considering no glacier distributes in Bam Co basin, we contribute its dynamic mostly to permafrost degradation preliminarily. For Puruogangri ice field, we measured the ice flow velocity and the glacier surface elevation by using SAR interferometry applied to ERS- / SAR Tandem images acquired in September 998. Fig. 3 shows the magnitude and spatial pattern of glacier velocity of the Purogangri ice field. A maximum surface velocity of 0. m/day (around 43.8 m/ year) was observed in glacier tongues of the eastern portion of the Puruogangri, with an averaged velocity of 0.07 m/day. However, the glaciers of the western centre portion experienced relatively stable patterns, which are consistent to the GPS observations obtained in 00 (Pu, 00) and our filed work in 0 (Figure 3). Five young students with Ph.D. and MSc level took part in this study. They were responsible for data collection, data processing and analysis. They undertook major research work. LI Gang, Ph.D. Student, he is responsible for processing SAR and altimeter data, collecting and analysing hydrological, meteorology and permafrost data. LIU Lin, Ph.D Student, he is responsible for SAR data processing and field observations for studying glacier dynamics. 3. Glacier flow velocity generated from ALOS/PALSAR imagery by feature tracking. Flow velocity and direction maps & flow velocity profile of main flow lines. Lake area change for Bam Co Lake. LOS velocity field for Bam Co Lake Area 3. Glacier surface velocity and Field work (up right) in Puruogangri ice field 54 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 55

29 ID 0455 atmosphere & Climate Atmosphere & climate ID 059 East Asia Monsoon & Air Quality Atmospheric Dynamics & Cities Dr. Nan Hao, Remote Sensing Technology Institute, German Aerospace Centre, Germany Prof. Ding Aijun, Institute for Climate and Global Change Research, Nanjing University, China Prof. Costas Varotsos, University of Athens, Greece Prof. Dr. Xue Yong, Institute of Remote Sensing Applications, Chinese Academy of Sciences, China Study Areas Three Chinese megacities Beijing, Shanghai and Guangzhou and other major cities such as Nanjing and Hongkong. Study Areas The study area of this project is whole Earth. Case studies for Athens, Greece (37 58 N 3 43 E) and Beijing, China ( N E) megacities are scheduled. We attempt to combine satellite observations throughout the troposphere with ozonesonde and aircraft measurements to provide a holistic view of the monsoon impact on tropospheric air pollutants over strongly affected regions of China. The air quality changes caused by the East Asian monsoon circulation and the potential impact of air pollutants over China on a regional climate change (the strength and tempo-spatial extension of the monsoon) will be investigated. IASI observations of tropospheric ozone over three Chinese megacities from 008 to 0 have been investigated. The different ozone seasonal patterns in Beijing, Shanghai and Guangzhou are consistent with ground-based measurement from other researchers. In Beijing the high ozone episodes are frequently observed during the summer, while in Guangzhou the maximum ozone occur in spring and autumn. It demonstrates the capability of satellite infrared nadir measurements to capture seasonal variations of tropospheric ozone in largely polluted regions. Ozonesonde data collected at HongKong during were analyzed to investigate the impact of East Asian Monsoon (EAM) on seasonal and interannual variations of tropospheric ozone in South China. MOPITT CO retrievals, Lagrangian dispersion modelling as well as EAM index were used to identify the roles of the monsoons on tropospheric ozone in this region. The EAM controls the seasonal cycle of the lower tropospheric ozone by long range transport of continental and marine air masses. The strength and onset of monsoons controlled the interannual variability of boundary layer ozone but showed contrast roles in spring and autumn, respectively. The EAM and biomass burning activities influence the interannual variability of free tropospheric ozone in Spring. Aerosol particles considered among the principal parameters of climate-dynamics and air pollution. An effective measure of aerosol particle content in the air has been established the Aerosol Index (AI). In this project, we examined the existence of time scaling in the AI time series globally as well over Athens and Beijing. Detrended Fluctuation Analysis (DFA) technique was applied to avoid the nonstationarity observed. The distribution of tropospheric ozone residual and nitrogen dioxide is also examined. DFA tool was applied to zonal mean daily AI values derived from satellite observations since 979 to search for self-similarity properties. The results show that the detrended and de-seasonalized AI fluctuations in both hemispheres and globally obey persistent longrange power-law correlations for time scales longer than about 4 days and shorter than about years. This suggests that the AI fluctuations in small time intervals are related to the AI fluctuations in longer time intervals in a power-law fashion (when the time intervals vary from about 4 days to about years). A plausible mechanism for the time scale of about years in AI could be the modulation of the Brewer Dobson cell by the quasi-biennial oscillation at the equatorial stratosphere in the zonal wind. The synoptic-scale meteorological systems probably give rise to the time scale of about 4 days. These findings could prove useful in testing the results of existing models, which should be examined to determine if they demonstrate the scaling behavior mentioned above. As far as the relationship between the tropospheric ozone residual and the nitrogen dioxide is concerned a de-correlation between them was found in the case of the data collected at the Athens basin. One Ph.D. student in LATMOS involved has a grant to work on O3 retrievals from IASI, for the study of local and regional pollution over China.. Climatology of O3, CO and Monsoon Index over Hong Kong during (top) and Seasonal distribution of tropospheric ozone in the Chinese megacities- Beijing, Shanghai and Guangzhou (bottom). IASI tropospheric ozone column for January over China One Post Doc student, four Ph.D. students and one MSc student are engaged in the project. Their mainly tasks are the derivation of the ground-based measurements, the retrieval of satellite-borne observations using appropriate models and the initial data processing.. Geographical distribution of tropospheric ozone residual for July 0 over mainland Greece as deduced from OMI observations.. Geographical distribution of nitrogen dioxide for July 0 over mainland Greece as deduced from OMI observations. 56 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 57

30 ID 056 atmosphere & Climate Atmosphere & climate ID 0577 co assessment in Ecosystem Chemisty Climate Change Steven A. Loiselle, Environmental Spectroscopy Group, University of Siena, Italy Ma Ronghua, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China Dr. Adj. Prof. Viktoria Sofieva, Finish Meteorological Institute, Finland Dr. Liu Chuanxi, Institute of Atmospheric Physics, Chinese Academy of Sciences, China Dr. Huang Fuxiang, National Satellite Meteorological Center, China Study Areas The study areas considered include the lakes and impoundments of the lower reaches of the Yangze River with particular attention to Lake Taihu in the Jiangsu Province. Study Areas Chemical composition and dynamics of the upper atmosphere and the lower stratosphere (UTLS) using satellite, ground-based and in-situ measurements, as well as chemistry-transport models. Harmful algal blooms continue to compromise the functioning of inland lakes and coastal environments. The causes of the increase in the frequency and extension of these blooms are not well understood, even through local and national governments have dedicated significant resources to improving catchment conditions. Due to the temporal and spatial nature of these blooms and their high inter-annual variability, clear evidence of improvement has not been presented, hampering the further development of integrated management strategies. We are presently exploring bloom estimation methods to better identify links to global and local drivers using MODIS, GOCI and MERIS data. In recent decades, algal blooms have caused significant economic and governance challenges in the cities around important lakes in the Jiangsu Province. In May 007, a massive cyanobacterial bloom of Microcystis overwhelmed the water treatment facilities of Wuxi city, leaving more than million people without drinking water for a week. Mitigation strategies to reduce nutrient loading and control eutrophication in the Lake Taihu were initiated in the 990s with an investment of.9 billion RMB to clean Lake Taihu by 00. The 007 drinking water emergency demonstrated that mitigation strategies were not effective. Using a new Floating Algae Index (FAI) algorithm, MODIS 50-m resolution Level-0 data from 000 to 0 was used to estimate algal blooms over Lake Taihu. The initial bloom date was defined as the date of the first observation where the FAI bloom area was greater than 50 km. The spatial extent of the algal blooms reached a maximum in 006, 007 and 00. Temporal and spatial decomposition analyses are being performed to obtain an improved understanding of the blooms dynamics over the study period. Global and regional driver datasets are being explored to identify links to bloom dynamics. Our project is dedicated to the upper troposphere and the lower stratosphere (UTLS), based on new satellite data and modern atmospheric models. Special scientific focuses of the project are: (i) dynamical and chemical structures of the extra-tropical UTLS and tropical tropopause layer (ii) multi-scale processes in the UTLS (iii) stratosphere-troposphere exchange (iv) stratospheric ozone variability and updated ozone climatology. A new linked climatology of ozone profiles and tropopause height (TpO3) has been created, based on ozone data from SAGE-II satellite and from ozone sondes. The TpO3 climatology provides a significantly improved representation of the separation between the troposphere and stratosphere. It is characterized by reduced variability in the UTLS. Other benefits of the TpO3 climatology are partial characterization of longitudinal variability and specific features in case of double tropopauses. The TpO3 climatology can be advantageous for use as a priori in satellite retrieval algorithms (as demonstrated for ozone profile retrievals from the Ozone Monitoring Instrument (OMI) instrument on board the Aura/EOS satellite) and for validating climate model simulations. Using a combination of ozonesonde data and numerical simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS), the trend of tropospheric ozone during over Beijing was investigated. Tropospheric ozone shows a winter minimum and a broad summer maximum with a clear positive trend in the maximum summer ozone concentration over the last decade. Transport rather than chemistry drives much of the seasonality of tropospheric ozone. However, transport processes alone cannot explain the significant trend of tropospheric ozone in the observations. Photochemical ozone production strongly contributes to the tropospheric ozone increase during spring and summer. Estimated daily maximum algal blooms in Taihu for the years 00, 005 and 0, estimated using Floating Algae Index Note where algal blooms are defined if FAI > in MODIS FAI imagery, and the results in Gong Bay and East Lake may be mixed by algal bloom and aquatic vegetation (Ma et al., 008 ), but the results in and East Bay must be aquatic vegetation. The MODIS were obtained and processed by junior scientists at NIGLAS, including Zhen Wang Jiawang Rao, Zeren Wang, Changfeng Wang, Jiahui Huang. This research is being performed by young scientist Dr. Hongtao Duan at the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences with the collaboration of Prof.s Ronghua Ma and Steven Loiselle.. Long-term trend of tropospheric ozone over Beijing, see text and [Wang et al., 0] for details. There is one European Ph.D. student working on the UTLS studies at FMI. There are three Chinese Ph.D. students working on this project at IAP. Dr. Yong Wang got his Ph.D. in July 0.. Example of ozone profiles form the new linked ozone-tropopause climatology TpO3[Sofieva et al., 03], for latitude range 60S-70S in September. LLM stand for the widely used climatology of McPeters et al.[007] 58 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 59

31 ID 0643 atmosphere & Climate Atmosphere & climate ID 0663 CO from Space AMFIC Dr. Hartmut Boesch, Space Research Centre, University of Leicester, UK Prof. Paul Palmer, School of GeoSciences University of Edinburgh, UK Prof. Liu Yi, Institute of Atmospheric Physics, Beijing, China Dr. Cai Zhaonan, Institute of Atmospheric Physics, Beijing, China Dr. Ronald van der A, Royal Netherlands Meteorological Institute, The Netherlands Dr. Bai Jianhui, Institute of Atmospheric Physics, Chinese Academy of Sciences, China Study Areas The planned Chinese Carbon Satellite (TanSat, Tan - carbon in Chinese) Mission will provide densely-sampled global column CO observations. TanSat is scheduled to be launched in 05. Study Areas The AMFIC project monitors and forecasts tropospheric pollutants over China. Satellite data, in situ measurements and model results are used to generate consistent air quality information over China. The main objectives of this project are to improve current forward modelling system and atmospheric retrieval algorithm for column CO, to test the algorithm with available satellite data from GOSAT, and finally improve the algorithm for the upcoming TanSat and other hyperspectral satellite-based instruments. The Chinese Team has developed a accurate retrieval algorithm for the column-averaged CO dry-air mixing ratio (XCO ) to be observed by TanSat Chinese Carbon Dioxide Observation Satellite that will be launched in 05. The Greenhouse Gases Observing Satellite (GOSAT) LB spectrum was applied in retrieval experiment, and the results were validated with ground-observed measurements from the Total Column Carbon Observing Network (TCCON). At mid-latitudes, most results fell in the % error region. Liu et al. (03). CO retrievals from GOSAT are also obtained by the UK team which show that satellite observations can map the global distribution of CO. Validation against ground-based TCCON data has shown that that the data has an accuracy better than ppm (Cogan et al., 0). However, for many regions including China there are currently no validation sites. Additional validations in China will be setup within the TanSat programme. CO soundings from GOSAT over China are shown in the figure above. Most prominent is the strong seasonal cycle with a minimum in summer due to the uptake of CO by vegetation. The main theme of the project is the study of air pollution over China using satellite observations. To study the sources of air pollution we will derive emissions of volatile organic emissions (VOC), HCHO, SO and NO in East-China and Beijing in particular. We will combine ground-based measurements, satellite retrievals and global/regional models, in order to gain insight in anthropogenic and biogenic emissions in the Beijing area. To study the effect of aerosols on the climate, several studies will be conducted to gain understanding of the distribution of aerosols in time and space, including the temporal variability and the vertical distribution. An algorithm has been developed for deriving NOx emission from satellite observations of NO (Mijling and van der A, 0). It is capable of detecting new emission sources such as power plants and ship tracks which are unaccounted for in the a priori emission inventory. Using OMI and GOME- data, the algorithm is able to detect emission trends on a monthly resolution. It has been applied to study emission changes during the 008 Beijing Olympic Games. Furthermore, the NO tropospheric concentrations calculated with the new emission estimates show better agreement with the observed concentrations, both in space as in time, facilitating the use of the algorithm in operational air quality forecasting. In preparations for the validation of biogenic emissions derived from satellite observations, local ground measurements in forest regions are performed. Biogenic volatile organic compound (BVOC) emissions were measured on an above canopy tower in a temperate forest, Changbai Mountain, Jilin province and in a subtropical bamboo forest, in Linan county, Zhejiang Province.. CO columns over China retrieved from observations of the GOSAT satellite instrument for June, September, December 009 and March Ph.D. students are developing the TanSat retrieval algorithm and carrying on the experiment of surface validation measurement.. TanSat-Chinese Carbon Dioxide Observation Satellite in the manufacture experiments A Chinese Ph.D. student is working on NO retrieval from a MAX- DOAS at Xianghe station (near Beijing). She has been visiting the European partner institute BIRA to learn more about the MAX-DOAS instrument. 3. Validation of biogenic emissions from forest measured at Changbai Mountain, Jilin.. Derived time series of NO emissions for Beijing area, China. 3. Participants of IBBAC workshop at IAP, Beijing in 0 60 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 6

32 ID 0603 atmosphere & Climate Atmosphere & climate ID 0603 CEOP-TPE Prof. Z.(Bob) Su, Change to ITC University of Twente, the Netherlands Prof. Ma Yaoming, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, China Study Areas As the roof of the world and the third pole of the earth, the Tibetan Plateau is well known both for its high altitude and unique geographical features, yet it is much less studied than its other high altitude counterparts. To improve the understanding of the interactions between the Asian monsoon, glaciers/permafrost and the Tibetan plateau atmosphere in terms of water and energy budgets in order to assess and understand the causes of changes in cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and to predict the possible changes in water resources in the Third Pole Environment. A parameterization scheme based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape. As a case study, the methodology was applied to the whole Tibetan Plateau (TP) area. Four images of MODIS data were used in this study for comparison among winter, spring, summer, and autumn. The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform (TORP). Therefore, reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology (Ma et al. 0a, Fig. 4). Simultaneously, a parameterization methodology based on NOAA/ AVHRR data and field observations is described and tested for deriving the regional surface reflectance, surface temperature, net radiation flux and soil heat flux over a heterogeneous landscape. As a case study, the methodology was applied to the Tibetan Plateau area. Two scenes of NOAA/AVHRR data were used in this study. The derived results were also validated using the ground truth. The results show that reasonable regional distribution of surface variables (surface reflectance and surface temperature), net radiation flux and soil heat flux over the heterogeneous landscape of the Tibetan Plateau can be obtained by using this methodology (Ma et al. 0b). With the Tibetan Plateau observatory of plateau-scale soil moisture and soil temperature (Tibet-Obs) and the existing coarse satellite products, a blending soil moisture product over the Plateau is generated. The Tibet-Obs consists of three regional scale in-situ reference networks, including the Naqu network in a cold semiarid climate, the Maqu network in a cold humid climate and the Ali network in a clod arid climate. The Tibet-Obs networks provide a representative coverage of the different climate and land surface hydrometeorological conditions on the Plateau. The satellite data used includes the AMSR-E soil moisture products by VUA-NASA and the ASCAT soil moisture products by TU Wien. A simple Bayesian based method is used to blend different satellite-derived (e.g. active & passive) soil moisture data (Fig. 5). Before applying the method, the bias in satellite data should be corrected by comparing satellite data with in-situ data. The bias correction is implemented with each (e.g. ground truth) of the three networks. The bias-corrected satellite data are then blended, and compared with the other two networks for validation. In addition, a merged observation data set, by considering different weights for different climate and land surface, is generated for bias correcting satellite data, which is subsequently blended and compared with the three networks. The statistics matrixes deduced by the four sets of datasets are used to evaluate the blending soil moisture product over Tibetan Plateau The location and landscape of the Tibetan Platea 4. Derived results from parameterization methodology based on MODIS data Biased corrected merged soil moisture products using retrievals from ASCAT data and AMSR-E data are used (unit: cm3 cm-3) -. TORP (Tibetan Observation and Research Platform) Lei Zhong, Associate Professor, Ph.D., Land-atmosphere water exchange process in Nagqu river basin of the central Tibetan Plateau area Weiqiang Ma, Associate Professor, Ph.D., Simulations of land surface parameters and related meteorological elements with WRF model over the Tibetan Plateau Binbin Wang, MSc., Improving the Surface Energy Banlance System (SEBS) model on the land surface interactions over the Tibetan Plateau Cunbo Han MSc., Estimating land surface energy fluxes over mountainous area of the Tibetan Plateau by using the satellite and in-situ data Yijian Zeng, Ph.D., Land surface processes and interactions with climate Xuelong Chen, Ph.D., Determination of land surface heat fluxes over the Tibetan Plateau area by integrating remote sensing with land surface meteorological data Laura Dente, MSc., Retrieval of soil moisture at global scale from satellite data acquired by passive and active microwave sensors Shaoning Lv, M.Sc., Soil moisture and its impact on the East Asia summer monsoon forecasting Donghai Zheng, M.Sc. Exploring downstream water availability of the Yellow River based on historic and projected runoff change in the source region area Ying Huang, M.Sc., Evaluation of hydrological response of the Yangtze river basin to climate change and human activities 6 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 63

33 ID 0470 coastal zones coastal zones ID 0555 Coastal Zones Wetlands Monitoring Dr. Juergen Fischer, Institute of Space Sciences, Freie Universitat Berlin, Germany Dr. David Doxaran, Laboratoire d Oceanographie ve Villefranche, France doxaran@obs-vlfr.fr Dr. Cui Tingwei, First Institute of Oceanography, State Oceanic Administration, China cuitingwei@fio.org.cn Ma Yi, First Institute of Oceanography, State Oceanic Administration, China mayimail@fio.org.cn Dr. Ir. Mhd. Suhyb, University of Twente, The Netherlands s.salama@utwente.nl Dr. David Doxaran, Laboratoire d Oceanographie ve Villefranche, France doxaran@obs-vlfr.fr Prof. Dr. Shen Fang, East China Normal University (ECNU), China Fshen@sklec.ecnu.edu.cn Prof. Zhou Yunxuan, East China Normal University (ECNU), China zhouyx@sklec.ecnu.edu.cn Study Areas China coast zone and the adjacent seas. Study Areas The Yangtze estuary is an important shipping route and diverse wildlife sanctuary. More than tonnes of fine sediments are deposited yearly in the estuarine area forming an extensive intertidal & wetland zone. Objectives of this project are to () develop the monitoring and assessment technology of land use/cover, wetlands, macroalgae bloom, river plume along China coast by synergistic use of optical, infrared and microwave satellite data supported by airborne and in situ observations; () disclose the spatio-temporal variations in coastal land cover and land use, wetlands, macroalgae bloom, and river plume as well as the possible relationships with global change. Extensive in situ optical and biogeochemical observations were performed in the four seasons of 0 along east China coast. MERIS ocean-color products were validated. At blue-green (~490nm) and green (~555nm) bands, MERIS retrievals of Rrs(λ) have lowest uncertainties with median of absolute percentage of difference (APDm) of 5%~7% and root-mean-square-error (RMS) of 0.00~ sr-, whereas the Rrs(λ) uncertainty at 4nm is highest (APDm 47%~6%, RMS 0.007~0.004 sr-). MERIS SPM (Chl-a) products underestimate (overestimate) the in situ measurements by 40% (54%) in the range of 0.5~0 g/m3 (0.3~5 mg/m3). Field work was carried out in autumn 0 to measure spectra and collect soil samples of different types of wetlands in the Yellow River delta. Spectral unmixing method was applied to PROBA CHRIS images to get wetlands classification results of the Yellow River delta. Factors of atmosphere correction and NDVI threshold were found to have significant influences on the satellite monitoring results of green macroalgae bloom. The objective is to use earth observation and in-situ measurements to characterize the biophysical properties of the Yangtze estuary (water column and mudflats) and understand their variability with respect to changes in hydrological cycle components. This includes: (i) mapping and measuring the changes in the biophysical characteristics over time; (ii) resolve the interrelationships between biophysical properties and changes in the hydrological cycle (iii), mapping the different ecohabitats within the estuary. The atmospheric correction model of Salama and Shen (00) is adapted and applied to the Yangtze estuary. This adapted model decomposes the reflectance at the near-infrared (NIR) wavelengths of MERIS into two components: aerosol and suspended particulate matter (SPM). The model then extrapolates the solution at the NIR to shorter wavelengths using lookup tables. We, initially, validate this method with in-situ measurements from the Poyang Lake in China. The determination coefficient, R, between derived and measured SPM concentrations is >0.94 with an RMSE value < 3 g.m-3 (Fig.). Our method derived realistic patterns of SPM dynamics in turbid waters and spatially un-correlated aerosol reflectance maps. The proposed method will be validated against data from the Yangtze estuary and applied to the full archive of MERIS data. SPM concentrations retrieved from MERIS satellite data will be compared to those retrieved from other ocean colour satellite sensors, i.e., using MODIS-Aqua and GOCI data atmospherically-corrected based on regionally-tuned algorithms (Wang et al. 0). Satellite observations will be validated and completed by bio-optical data recorded along the water column by autonomous profiling floats. Fig. shows an example of the validation data as measured by the Provbio profiling float in 0.. In-situ diurnal variations of SSS (top), SPM (centre) and chlorophyll-a (bottom) in the middle of the East China Sea. Field work in Yellow river delta and validation results of MERIS Rrs(λ) and SPM in YS&ECS 3 Ph.D and 7 MSc are involved in the project. They undertook the tasks of in situ data collection, satellite image processing, and data analysis.. Green macroalgae bloom in HJ- CCD image on May 8th,0 An MSc project will research the dynamics of SPM in the Yangtze Estuary as retrieved from the 0 years archive of MERIS data. The MSc research will start in August 03 at ITC and will be supervised by ITC (NL) and LOV (FR).. MERIS reflectance NIR wavelengths; aerosols (left) SPM backscattering coefficient (right) 64 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 65

34 ID 0558 coastal zones coastal zones ID 0593 EPHESUS Data & Models Synergy for Coastal Dynamics Dr. Federica Braga, Institute of Marine Sciences, Venice, Italy Dr. Luigi Tosi, Institute of Marine Sciences, Venice, Italy Dr. Xing Qianguo, Yantai Institute of coastal zone research, Chinese Academy of Sciences, China Prof. Johnny A. Johannessen, Nansen Environmental and Remote Sensing Center, Norway Dr. Fabrice Collard, OceanDataLab, France Prof. Zhou Yunxuan, State Key Laboratory of Estuarine and Coastal Research, East China Normal University, China Prof. He Mingxia, Ocean Remote Sensing Institute, Ocean University of China, China Study Areas The coastal zone of the southern Bohai Sea and its adjacent areas, including the Yellow River Delta, the Laizhou Bay and the Shandong Peninsula, China. Study Areas Yangtze River mouth, for monitoring of freshwater runoff, shallow water bathymetry, bio-optical constituents, and river mouth salinity using Earth observation data. The main topic of the research programme is the identification, through Remote Sensing data, of the key parameters able to highlight the effects of continental and marine waters exchange in coastal ecosystem (i.e., saltwater contamination, inland water discharges on the sea, coastline changes). The characterization and the monitoring of significant ecological and physical parameters can support the understanding of the interactions between hydrological processes and the evolution of the coastal zones PROBA/CHRIS multi-angle hyperspectral images since 009, 9 HJ-A, B multi-spectral images since 0, and 3 Landsat MSS, TM, ETM+ images since 976 were collected and processed for further investigation of the coastline and vegetation at the YRD. The coastline vector was manually extracted from the historic maps. Three field campaigns at the YRD were conducted to validate the characteristic features against satellite images: one was in the Autumn, 0, and two in the Winter (0-03). One sea-truth campaign was carried out to measure the water optics (light attenuation coefficients, back scattering, etc) at the Sishili Bay. Under the influence of deposition of suspended matter, erosion of coastline and embankment works, the distribution of vegetation varied with time dramatically. In the past 60 years, about 800 km of new land developed at the YRD. After the Yellow River changed its course in 976, deposition was replaced by erosion in the north and northeast, the distribution of vegetation decreased. However, the suspended matter brought by the Yellow River developed new land in the southeast, which expanded the vegetation distribution. In the late 980 s, artificial levee and jetty were built in the east and northeast, which restrained the erosion significantly, so that the vegetation distribution seldom changed significantly since then. Estimating and monitoring the Yangtze River freshwater runoff (surface speed and speed variability) from the SAR based range Doppler shift anomaly observations and development and validation of a regionalto-local bio-optical algorithm for retrieval of chlorophyll, suspended minerals and dissolved organic carbon (DOC) from ocean color remote sensing data. The Nansen Environmental and Remote Sensing Center has developed new open-source Python software for reading and processing of geospatial raster data from satellite sensors, called NanSat. In particular, the software configures the data in compliance with known standards such as the Netcdf-CF convention and adds scientific meaning to the data. It is a scientist-friendly command-line based toolbox, which facilitates easy development and testing of scientific algorithms, easy analysis of geospatial data, and efficient operational processing including synergistic use of several types of geospatial raster data. As such, the tools for retrieving geophysical parameters (e.g. bio-optical constituents; Figure ) over the Yangtze River runoff from several satellites (e.g. SMOS, Radarsat-, and Sentinel- as well as reprocessed Envisat ASAR acquisitions) are readily available, and new results may in the near future be expected. The NERSC algorithm for retrieval of water quality parameters applies a hydro-optical model that was developed for several water bodies (the North Sea, Lake Ladoga, Lake Michigan, etc). The hydro-optical model consists of spectral values of specific absorption and backscattering coefficient of chlorophyll-a, suspended mineral matter, dissolved organic carbon, and other. Preliminary results show that even without tuning, the algorithm can be applied to optical remote sensing data taken over the Yangtze estuary and successfully retrieve concentrations of three parameters: chlorophyll-a (CHL), total suspended minerals (TSM), dissolved organic carbon (DOC) as shown in Fig.. One Ph.D. student (Mr. Dingfeng YU) and two Msc students (Mr. Shaopeng LI, Ms. Mingjing LOU) joined the field campaigns and learned about the optical measurements and sampling. Dr. Qiang SUN participated in the cruise at the Sishili Bay. 3. The coastline (red lines) at the southern Bohai Sea 60 years ago with a current HJ-A multi-spectral composite image acquired on -Oct-0; the green dots are the sampling site in October, 0; and the pink dots are the sampling sites in January, 03.. Probes used to measure the reflectance of salt marsh in October, 0, and 3. Ice-covered wetland of Yellow River Delta in Winter (0-03).. Concentration of CHL, TSM and DOC retrieved with the BOREALI algorithm, , East China Sea.. Range Doppler velocity estimates from the river mouth of the Amazon Anton Korosov, Ph.D., and Morten W. Hansen, PhD: Development and tuning of software and algorithms, and processing and analysis of remote sensing data. Bo Tian, PhD: SAR applications and in-situ measurement of physical and biological parameters. From Feb. 03, Wang Lihua (ECNU post. doc.) is undertaking a 6-month research fellowship at ESA-ESRIN. She is investigating SAR Doppler shifts for the East China Sea using ASAR WS data. 66 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 67

35 ID 0644 coastal zones coastal zones ID 0705 Reclaimed Land Monitoring OPAC Dr. Antonio Pepe, The Institute for Electromagnetic Sensing of the Environment, Italy Dr. Zhao Qing, School of Resources and Environment Science, East China Normal University, China Prof. Werner Alpers, Institute of Oceanography, University of Hamburg, Germany Prof. Tang DanLing, South China Sea Institute of Oceanology, China Dr. Riccardo Lanari, The Institute for Electromagnetic Sensing of the Environment, Italy Dr. Gao Wei, School of Resources and Environment Science, East China Normal University, China Dr. Samantha Lavender, Spatially Scientific Ltd, UK Prof. Chen Chuqun, South China Sea Institute of Oceanology, Chinese Academy of Sciences, China Study Areas The coastal reclaimed areas in Shanghai nearby the Shanghai Lingang New City, which are the results of the world largest land reclamation project with 45% of the city area that has been reclaimed from the sea. Study Areas South China Sea (in particular: Pearl River Estuary), East China Sea, Yellow Sea, Bohai Sea, Upwelling area north of Taiwan. The project is aimed at investigating the residual settlement of reclaimed foundation and coastal line change in Shanghai through advanced PSI-DInSAR approaches (PSI and SBAS). The main research goals of the planned activities are to: ) Generate deformation maps in costal reclaimed areas of Shanghai through DInSAR; ) Validate deformation results through field work; 3) Analyze the validated DInSAR results in detail; 4) Investigate the mechanisms that are responsible for ground deformation in reclaimed foundation. We performed a PSI-DInSAR investigation on the Shanghai area by using an archive of 46 SAR scenes acquired by the ENVISAT ASAR sensor from 003 to 00 over the Shanghai city area (Fig. (a)). We retrieved the deformation map of the Lingang New city (Fig. (b)), by processing the SAR data-set through the Persistent Scatterer technique. A total number of 370 persistent scatterers were detected, with an average density of about 30 scatterers km-. The obtained map reveals a maximum subsidence rate of about 0.5 mm/year, which is in general agreement with the area annual subsidence rate as measured from 996 to 00 (0-0 mm/year). To have a clear picture of the deformation signals affecting the whole city of Shanghai, we complement the PS deformation measurements with the ones relevant to distributed targets (DS), as retrieved by processing the available SAR data-set via the SBAS-DInSAR procedure. As a result, we obtained the spatially-dense deformation map shown in Fig. (c) (with a resolution of 80 x 80 m). The comparison between PS and DS deformation measurements is still in progress and represents one of the goals of the project.. The project has four objectives: Detection of ) anthropogenic pollution of the sea, like oil pollution and land-based pollution, ) phytoplankton blooms, and 3) macro-algal blooms in the Chinese Seas using spaceborne synthetic aperture and optical/infrared images, and 4) monitoring the degradation of coral reefs in the South China Sea. One of the main activities in the first months has been to screen the ESA archive for ERS/ and Envisat SAR images showing radar signatures of land-based pollution and of oil spill look-alikes originating from biogenic surface films, Several locations at the coast of Mainland China and of Taiwan have been identified showing recurrent patterns of reduced radar backscattering. They are usually attached to the mouth of rivers which carry heavy loads of pollutants. Polluted areas become visible on SAR images as dark patches. Two examples of such SAR images acquired over the Pearl River delta are depicted in Figs. a and b. Furthermore, it has been shown that areas of reduced radar backscattering are often located in upwelling areas. Here cold, nutrientrich water rises to higher water levels causing there an increase in plankton and fish population, Marine biota exudates surface active material which rises to the sea surface and forms there biogenic surface films which are only one-molecular layer thick. These monomolecular surface films can damp short surface waves as strongly as mineral oil films. An example of a large dark area located in an upwelling area in the Strait of Taiwan along the coast of Mainland China is depicted in Fig. c. Here the chlorophyll-a (Chl-a) concentration (Fig. d) and thus the marine biota is strongly enhanced. a b c Envisat ASAR IM images acquired on 6 June 008 (a) and 3 July 008 (b) over the Pearl River delta, and over the Strait of Taiwan on Aug. 0 (c). Chl-a map derived from MERIS data acquired on Aug. 0(d). At ECNU Mr. Chong YOU (MSc) research activities are related to multi-sensor DInSAR deformation time-series analysis; Ms. Yu Liu (Ph.D. student) contributes to retrieve coastal line changes with SARs. At IREA two Post-Doc researchers (dr. M. Bonano and dr. F. Calo) are involved in the generation and analysis of SBAS DInSAR products.. PSI DInSAR results: (a) Lingang New city reclaimed area; (b) PS ground settlement map of the analyzed area; (c) SBAS deformation map of the Shangai city area, where the Lingang New city zone is evidenced by a black box. Gang Pan, Ph.D. South China Sea Institute of Oceanology: Analysis of radar signatures of oil spills from ships. Jinrou Li, South China Sea Institute of Oceanology: Analysis of biological activity as evidenced by high Chl-a concentration. 68 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 69

36 ID 04 oceanography oceanography ID 0466 Ocean Resources & Microwave RS Applications of RA data Dr. Bertrand Chapron, French Research Institute for Exploitation of the Sea, France Dr. Fabrice Collard, OceanDataLab, France Dr. Yang Jingsong, Satellite Ocean Environment Dynamics Second Institute of Oceanography, China Dr. Meng Junmin, First Institute of Oceanography, State Oceanographic Administration, China Mr. Bernard Martinez, IsardSAT S.L, Spain Dr. Yang Jungang, First Institute of Oceanography, SOA, China Prof. Liao Jingjuan, Center for Earth Observation and Digital Eartch, Chinese Academy of Science, China Study Areas China Seas and adjacent waters, and China coastal zones are the main study areas. Other areas including Pacific Ocean, Indian Ocean, Atlantic Ocean, and Southern Ocean are also concerned. Study Areas The study areas are the Qinghai-Tibetan Plateau (QTP) containing thousands of small and big lakes and the Chinese sea including Bohai Sea, Yellow Sea, East China Sea and South China Sea. ) To investigate winds, waves, currents and mesoscale processes in China Seas with SAR, Altimetry and Scatterometry from China, ESA and TPM; ) To develop ocean wave energy resource evaluation method with remote sensing data. To get ocean wave energy resource distribution map of China Seas; 3) To analyze coastlines changes in the China coastal zones especially Yangzi River Estuary due to reclamation; 4) To prepare CFOSAT by using SAR and Scatterometry data Envisat SAR imagery, and MTSAT and FY- IR imagery were used to examine the typhoons in the Western North Pacific from 005 to 0. Nine cases of various typhoons in different years, locations, and conditions have been used to compare the typhoon eyes by SAR (on the ocean surface) with IR (at the cloud-top level) images. Furthermore, the best track data getting from the Joint Typhoon Warning Center (JTWC), Chinese Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA) are checked for the calibration and validation. Large horizontal distance between typhoon eyes on the ocean surface and on the cloud top is found. Quality assessment of HY-A SCAT wind products is presented through the comparison of the first six months operationally released SCAT products with in situ data from the National Data Buoy Center (NDBC) buoys, R/V Polarstern, Aurora Australis, Roger Revelle and PY30- oil platform. HY-A SCAT wind speed bias of m/s, and RMSE of.3 m/s are found in comparison with NDBC moored buoys. For wind speed higher than 3 m/s, the HY- SCAT wind direction RMSE of 9.9 with a bias of 0.9 in respect to buoy winds is presented. These results have been confirmed through comparisons with other in situ measurements, including from R/V Polarstern, Aurora Australis, Roger Revelle and PY30- oil platform. The first objective is developing the processing method of altimeter products suiting for the application in the Chinese sea, to monitor and detect the sea-level, ocean tide, ocean wave, mesoscale eddies combining the ESA and Chinese data. The second objective is measuring lakes height change on the QTP. Since many lakes are remote and difficult to access, sa radar altimetry is effectively used for monitoring the water level change of fifty-one lakes in the QTP. On the application of altimetry data in the Chinese Sea, all the ESA s altimeter data and Chinese HY- altimeter data are collected and under the processing. Based on the AVISO merged altimeter data, the sea level change in the East China Sea and mesoscale eddy in the South China Sea are studied in order to validate the methods. The research s results show the sea level in the East Chinese Sea is rising at rate of 3.8mm/year from 99(Wang et al., 0), and the number of cold and warm mesoscale eddies are almost the same with most distributing in the northern of South China Sea. On the application of altimetry data in the Chinese lakes, the robust least square method can effectively remove the abnormal points in observation series data and make the long-term trend estimation more accurate when seasonal changes of lake level are considered. We found that southern lakes level decrease, northern lakes level increase, and most lakes level increase and small part lakes level decrease in central plateau.. Locations of the in situ observations used in the validation. 49 NDBC buoys and PY30- oil platform are marked with dots and pentagram, respectively. The cruise track of R/V Polarstern, Aurora Australis and Roger Revelle are plotted with blue, green and yellow lines, respectively. Dr. Antony K. Liu retired from NASA is now a Visiting Professor from SOED and a Qiushi Chair Professor from Zhejiang University. Yufang Pan (Msc.) is a young scientist from State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, SOA. He Wang (Msc.) is a young scientist from National Ocean Technology Center, SOA.. Typhoon Megi collected over the southeast of Taiwan on October 7, 00 from (a) MODIS, (b) MTSAT, and (c) Envisat. The typhoon eye is a dark circle in (d) the zoom-in Envisat SAR image One Ph.D. student and two MSc students, Long Wang and Lina Liu, are involved in the study of Chinese seas. Two young students with Ph. D and MSc level took part in the study and carried out lake level extraction and related to data processing.. the distribution of mesoscale eddies in the South China Sea in 0detected by altimeter data(red is warm eddy and blue is cold. Ground tracks of ENVISAT RA and HY- altimeter in the Chinese Sea 70 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 7

37 ID 0580 oceanography oceanography ID 0689 Marine Safety & Security Oil Spill Monitoring Dr. Susanne Lehner, German Aerospace Centre, Germany Dr. XiaoMing Li, German Aerospace Centre, Germany Prof. He Ming-Xia, Ocean Remote Sensing Institute, Ocean University of China, China Dr. Ren YongZheng, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, China Prof. Jon Atli Benediktsson, University of Iceland, Iceland Dr. Ferdinando Nunziata, Università di Napoli Pathenope, Italy Prof. Chi Mingmin, Fudan University, China Dr. Wei Yongliang, College of Marine Sciences, Shanghai Ocean University, China Study Areas The Study areas include China and European Seas, for which sea cruises and surface measurement campaigns have been organised to validate the geo-physical retrievals such as Sea Surface Salinity and wind speed. Study Areas We detect oil spills in the global oceans with emphasis on coastal seas. Recent studies are focused on oil spills in Bohai Sea, China and Gulf of Mexico. The project aims to measure marine-meteo parameters such as sea surface wind, wave and salinity, to detect oil spills from offshore platforms or hazardous and illegal substances resulting from shipping, to monitor coastal algae blooms, and to identify ships of different sizes by utilizing the satellite observations from ESA and NRSCC data, as well as from the third party missions. Focus is on using the spaceborne microwave radar to support marine environmental safety in Chinese and European seas. We developed a nonlinear Geophysical Model Function (GMF), denoted XMOD, to retrieve the sea surface wind field from X-band TerraSAR-X/ Tandem-X data (TS-X/TD-X) to a good accuracy with a bias of -0.3 m/s, an RMSE of.47 m/s, and a scatter index of 6.0% [Li and Lehner, 03]. During August and September in 0, we carried out a comprehensive campaign in the South China Sea to validate the derived Sea Surface Salinity (SSS) from the ESA satellite of SMOS [Ren et al., 03], and the sea surface wind field from the German satellites of TS-X/TD-X data [Lehner et al., 03]. The SSS derived from the SMOS data shows a good agreement with the in situ measurements of CTD with a bias of 0.3 psu and an RMSE of 0.7 psu, respectively. We derived the monthly averaged SSS from SMOS data over the SCS in the period of Mar. 0 to Feb. 0, as shown in Fig.. Prior to the starting of the campaign, we ordered 3 TS-X/TD-X images according to the cruise route. One TD-X image was acquired over the typhoon Nanmadol in the Luzon Strait. Plot in the right panel of Fig. shows the retrieved TD-X sea surface wind field over Nanmadol using XMOD. Design multi-polarimetric SAR techniques for observing oil slicks; Illustrating the potentials of optical RS data by monitoring oil slicks off the Chinese coasts; Demonstrating the benefit of the fusion between polsar and optical data; Implementing operational drifting models to simulate the trajectory of oil slicks under different wind conditions; Developing different machine learning algorithms for detection of oil slicks; Fast learning the detection algorithms by development of efficient implementations with high performance computing architectures. Several oil spill accidents occurred at two oil platforms in Bohai Sea, China on June 4 and 7, 0 (Fig. ). The oil spills were subsequently observed by different types of satellite images including SAR, Chinese HJ--B CCD and NOAA MODIS. In order to study the fate of the oil spills, two numerical simulations were performed to simulate their trajectories with General NOAA Operational Modeling Environment (GNOME) model. We drive GNOME with currents obtained from an operational ocean model (Navy Coastal Ocean Model, NCOM) and surface winds from operational scatterometer measurements (the Advanced Scatterometer, ASCAT) for the first time. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June and 4. Three oil slicks are tracked simultaneously and our model results show good agreement with subsequent satellite observations in Bohai Sea (e. g. Fig. ). Moreover, GNOME simulation shows that the number of splots denoting the extent of spilled oil, is a vital factor for model stability when it is less than 500. Therefore, information of spill area obtained from satellite images, especially SAR, is an important factor for setting up the initial model conditions.. Monthly average SSS derived from SMOS data, South China Sea. Wind speed from TD-X ScanSAR image over the typhoon Nanmadol in the Luzon Strait on Aug. 7, 0 at 09:49 UTC (right) Mr. Domenico Velotto, the Ph.D. student at DLR, is responsible for oil spill and ship detection using spaceborne TerraSAR-X/ TandDEM-X data in the framework of Dragon 3. Six Ph.D. students and ten M.Sc. students are involved. They will partly join in the developments of oil detection algorithms, data acquisition and validation of results.. Detected oil spills (in colours) and model simulated results (in dots). The oil spills obtained from ENVISAT- SAR on June, 0 7 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 73

38 DRAGON 3 Projects References Projects References DRAGON 3 ID. 030 [] Dehecq, A., Gourmelen, N., Shepherd, A., Wingham, D.Evaluation of CryoSAT- for height retrieval over the Himalayan glaciers, ESA-CLIC-EGU conference, Nov 0, Frascati. ID [] Wooster, M. J., G. Roberts, G. L. W. Perry, and Y. J. Kaufman (005), Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release, J. of Geophysical Research, Vol.0. [] S. S. Kumar, D. P. Roy, L. Boschetti and R. Kremens (0), Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations, J. of Geophysical Research, Vol. 6. ID [] Z. G. Bai, D. L. Dent, L. Olsson, M. E. Schaepman, 008. Proxy global assessment of land degradation. Soil Use and Management, 4, [] Liu, H. J., Zhou, C. H., Cheng, W. M., Long, E., Li, R., 008. Monitoring sandy desertification of Otindag Sandy Land based on multi-date remote sensing images. Acta Ecologica Sinica. 8(), ID. 04 [] Cheng, Y.-H., Huang S.-J., Liu A. K., Ho C.-R., and Kuo N.-J. (0). Observation of typhoon eye on the sea surface using multi-sensors, Remote Sensing of Environment, Vol. 3, [] Jiang, X. W., Lin, M. S., Liu, J,Q., et al. (0). The HY- satellite and its preliminary assessment. International Journal of Digital Earth, Vol. 5, ID [] Z. Gao, X. Xu, J. Wang, H. Yang, W. Huang, H. Feng. (0). A method of estimating soil moisture based on the linear decomposition of mixture pixels. Mathematical and Computer Modelling. Doi: 0.06 / j.mcm ID [] Zhou, D.R., A.J. Ding et al., Impacts of the East Asian Monsoon on Seasonal and Interannual Variations of Lower Tropospheric Ozone over South China, to be submitted to Geophys. Res. Lett. [] S.Safieddine et al., Tropospheric ozone and nitrogen dioxide measurements in urban and rural regions as seen by IASI and GOME-, submitted to J.Geophys. Res. ID [] Wang L., Wang J. and Yang J G.(0). The Comprehensive Analysis of Sea Level Change in East China Sea. ACTA OCEANOLOGICA SINICA, (accepted, In Chinese) [] Zhang G, Xie H, Duan S, Tian M, Yi D. 0b. Water level variation of Lake Qinghai from satellite and in situ measurements under climate change. Journal of Applied Remote Sensing, 5: DOI: 0.7/ ID [] Cui T., Zhang J., Tang J., Sathyendranath S., Groom S., Ma Y., Zhao W., Song Q. Assessment of Satellite Ocean Color Products of MERIS, MODIS and SeaWiFS along East China coast (in the Yellow Sea and East China Sea). Submitted [] Cui T., Zhang J., Sun L, Jia Y., and Wang Z. (0). Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multisource data and drifting velocity estimation. IJRS, 33(7), ID. 050 [] Xu H. (008). A new index for delineating built-up land features in satellite imagery, IJRS, Vol. 9, [] Stathopoulou, M. and Cartalis, C. (009). Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. RSE, Vol. 3, ID. 055 [] Zhou, X.N., et al., Application of geographic information systems and remote sensing to schistosomiasis control in China. Acta Tropica, (): p [] Yang, G.J., Vounatsou, P., Tanner, M., Zhou, X.N., Utzinger, J., Remote sensing for predicting potential habitats of Oncomelania hupensis in Hongze, Baima and Gaoyou lakes in Jiangsu province, China. Geospatial health, 006., ID. 059 [] Arsov K., Sünkel H.: Influence of the DEM resolution in Gravity Reductions on Geoid Accuracy (998) Mathematical Geodesy and geoinformatics- University Press Graz. [] HU Shumei, WEN Hanjiang, LI Hongchao, SHI Xiaoyu, 0. Inversion of gravity anomalies over south China sea by use of combination of multisatellite altimeter data, Journal of Geodesy and Geodynamics, Vol.3(4), p56-59 (in Chinese). ID. 059 [] Varotsos, C. A., Ondov, J. M., Cracknell, A. P., Efstathiou, M. N. and Assimakopoulos, M. N. (006). Long-range persistence in global aerosol index dynamics. IJRS, Vol. 7, ID. 053 [] Witschas, B., Lemmerz, Ch., and Reitebuch O. (0): Horizontal lidar measurements for the proof of spontaneous Rayleigh-Brillouin scattering in the atmosphere. Appl. Opt., Vol. 5, [] Liu, Z.-S., Liu, B.-Y., Wu, S.-H., Li, Z.-G., Wang, Z.-J. (008). High spatial and temporal resolution mobile incoherent Doppler lidar for sea surface wind measurements. Opt. Lett., Vol. 33, ID [] Salama, M.S. and Shen, F. (00) Simultaneous atmospheric correction and quantification of uspended particulate matters from orbital and geostationary earth observation sensors. Estuarine, Coastal and Shelf Science, 86, (3), [] Wang, M., Shi, W. and Jiang, L. (0). Atmospheric correction 6 using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region. Optics Express, 0(), ID [] Huber C., Li Jiren, Daillet S., Chen Xiaoling, Lai Xijun, Crétaux JF, Zhang Wei, Uribe C., Stuber M. Huang Shifeng, Averty S., Burnham J. and Yésou H., 03. Twelve year of water resource monitoring over the Yangtze middle reaches exploiting DRAGON time series and field measurements. Proceedings of DRAGON final results, SP ESA 704. [] Zhao, M.J., Cong, P.H., Barter, M., Fox, A.D. and Cao, L. 0: The changing abundance and distribution of Greater White-fronted Geese Anser albifrons in the Yangtze River floodplain: impacts of recent hydrological changes. Bird Conservation International, Vol, Issue 0 pp ID [] F. Braga, Q. Xing, M. Lou, P. Shi, P. Teatini, L. Tosi, L. Zaggia, S. Donnici, M. Bresciani, S. Pascucci, S. Li, Z. Gao, D. Yu, C. Tang, D. Wang, Q. Sun, 0. EPHESUS: Remote sensing for coastal processes in Southern Bohai Sea China. Proceeding of the conference An Ancient Cultural Heritage and the Challenge for Future Development, -4 October 0. ID. 056 [] Duan, H.T., Ma, R.H., Xu, X.F., Kong, F.X., Zhang, S.X., Kong, W.J., Hao, J.Y., & Shang, L.L. (009). Two-Decade Reconstruction of Algal Blooms in China s Lake Taihu. Environmental Science & Technology, 43, [] Zhang, M., Duan, H., Shi, X., Yu, Y., & Kong, F. (0). Contributions of meteorology to the phenology of cyanobacterial blooms: Implications for future climate change. Water Research, 46, ID [] Liao, M., Tang, J., Wang, T., Balz, T. and Zhang, L. (0).Landslide monitoring with high-resolution SAR data in the Three Gorges region.science China Earth Sciences, Vol. 55, [] Liao, M., Pei, Y., Wang, H., Fang Z. and Wei, L. (0). Subsidence Monitoring in Shanghai Using the PS-InSAR Technique. Shanghai Land &Resources, Vol. 33(3), 5-0. ID [] Sofieva, V.F., et al. (03): A linked climatology of ozone profiles and tropopause height, submitted to J. Geophys. Res. [] Wang, Y., et al. (0): Tropospheric ozone trend over Beijing from 00 00: ozonesonde measurements and modeling analysis, Atmos. Chem. Phys., (8), ID [] Li, X.-M. and Lehner, S. (03). Sea surface wind field by TerraSAR-X and Tandem-X data: Algorithm development. IEEE TGRS, in revision. [] Ren, Y.-Z., Li, X.-M. and Dong, Q. (03). SMOS Sea Surface Salinity Validation in the South China Sea. Proceedings of Dragon Programme final results 008-0, ESA-SP 704, 03. [3] Lehner, S., Li, X.-M., Ren, Y.-Z. and Dong, Q. (03). Sea surface wind field by X-band TerraSAR-X and Tandem-X. Proceedings of Dragon Programme final results 008-0, ESA-SP 704, 03. ID [] Fang Shen, Yunxuan Zhou, Daoji Li, Weijian Zhu, MhD Shuhyb Salama, MERIS estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary, Int. J. of Remote Sensing, 00, 3(7-8): [] J.A. Johannessen, B. Chapron, F. Collard, V. Kudryavtsev, A. Mouche, D. Akimov, and K.-F. Dagestad (008), Direct ocean surface velocity measurements from Space: Improved quantitative interpretation of Envisat ASAR observations, Geophysical Research Letter, 8 November, 008. ID [] Ma, Y., L. Zhong, Y. Wang, Z.Su, 0, Using NOAA/AVHRR data to determine regional net radiation and soil heat fluxes over the heterogeneous landscape of the Tibetan Plateau, International Journal of Remote Sensing, 33(5): [] Ma, Y., B. Wang, L. Zhong, W. Ma, 0, The regional surface heating field over the heterogeneous landscape of the Tibetan Plateau using MODIS and in-situ data, Advances in Atmospheric Sciences, 9(): [3] Ma, Y., L. Zhong, B. Wang, W. Ma, X. Chen, and M. Li, 0, Determination of land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau by using the MODIS and in-situ data, Atmos. Chem. Phys., : [4] Dente, L., Vekerdy, Z., Wen, J., Su, Z., 0, Maqu network for validation of satellite-derived soil moisture products, International journal of applied earth observation and geoinformation: JAG, 7, ID [] Jia, K., Li, Q.Z., Tian, Y.C., Wu, B.F., Zhang, F.F., Meng, J.H. (0). Crop classification using multi-configuration SAR data in the North China Plain, IJRS, Vol. 33, ID [] Perski Z., Borkowski A., Wojciechowski T., Wójcik A., 0: Application of persistent scatterers interferometry for landslide monitoring in the vicinity of Roznow Lake in Poland. Acta Geodyn. Geomater., Vol. 8, No. 3 (63), [] Bai S.B., Wang J., Zhang Z. G., Chen C., 0: Combined landslide susceptibility mapping after WenChuan Earthquake at the Zhouqu segment in the Bailongjiang Basin, China, Catena, 99: 8 5. ID [] Feng et al. (03), The 0 Mw 6.8 Burma earthquake: Fault constraints provided by multiple SAR techniques GJI, under revision. [] Wen et al (0), Postseismic motion after the 00 MW 7.8 Kokoxili earthquake in Tibet observed by InSAR time series, JGR, 7, B ID [] Wang P, Li Y, Hong W, Supervised polarimetric SAR classification method based on Fisher linear discriminant, Journal of Beijing Institute of Technology, 0, Vol., No., pp64-pp68. [] Chen L., Zhang J., Li Y., Hong W., General Calibration Algorithm for Single- Transmitting-Dual-Receiving Polarimetric SAR System, Journal of Radars, 0, Vol., No. 3, pp33-pp38. ID. 06 [] Van der Velde, R., Su, Z., Van Oevelen, P., Wen, J., Ma, Y., and Salama, M.S., 0, Soil moisture mapping over the Central Part of the Tibetan Plateau using a series of ASAR WS images, Remote Sensing of Environment, 0, pp [] Kerr, Y. H., Waldteufel, P., Richaume, P., and co-authors, 0, The SMOS soil moisture retrieval algorithm, IEEE Transactions on Geoscience and Remote Sensing, 50, pp ID. 06 [] Zongli, J., Shiyin, L., Sichun, L., & Xin, W. (0). Estimate Yengisogat Glacier Surface Flow Velocities Using ALOS PALSAR Data Feature-tracking, Karakoram,China. Procedia Environmental Sciences, Vol., [] Luckman, A., Quincey, D., & Bevan, S. (007). The potential of satellite radar interferometry and feature tracking for monitoring flow rates of Himalayan glaciers. Remote Sensing of Environment, Vol., THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 75

39 DRAGON 3 Projects References Projects References DRAGON 3 ID [] Jinlong F. and Mingwei Z. (0). Supporting Agricultural Monitoring in APEC with FengYun Satellite data. Workshop on the application of Remote Sensing and GIS on Crops Productivity among APEC Economies, Beijing, China, 30-3 July 0. [] Yannick C., Allard J., Gregory D. and Pierre, D. (0). Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agricultural and Forest Meteorology, Vol. 5, ID ID [] Liu, P. et al., 03. Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China. IJAEOG, [] Li, X.-F. et al., 0. Measuring displacement fields from TerraSAR-X amplitude images by subpixel correlation: an application to the landslide in Shuping, Three Gorges area. Acta Petrologica Sinica 7(): ID estimation. International Journal of Remote Sensing, Vol. 7, [] M. Suan Moran and Ray D. Jackson, (99). Evaluation of Simplified Procedures for Retrieval of Land Surface Reflectance Factors from Satellite Sensor Output. Remote. Sens. Environ, 4: [3] Robert M. Haralick, Senior Member, (979), Proceedings Of The Ieee, Vol, 67, ID [] Taoyong Jin, Jiancheng Li, Weiping Jiang and Yonghai Chu (0). Lowfrequency sea level variation and its correlation with climate events in the Pacific. Chin. Science Bull., 57(7): ID [] Alpers, W., & H. Espedal (004), Oils and Surfactants, Chapter in Synthetic Aperture Radar Marine User s Manual, NOAA/NESDIS, USA, ISBN X, [] Wei, G.F., Tang, D.L., & Wang, S.F. (008), Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas, Advances in Space Research, -9. (SCI). [] LIU Yi, YANG DongXu & CAI ZhaoNan (03). A retrieval algorithm for TanSat XCO observation: Retrieval experiments using GOSAT data. Chinese Science Bulletin, doi: 0.007/s y. [] Cogan, A.J., H. Boesch, R. J. Parker, L. Feng, P. I. Palmer, J.-F. L. Blavier, N. M. Deutscher, R. Macatangay, J. Notholt, C. Roehl, T. Warneke, D. Wunch, Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite: Comparison with ground-based TCCON observations and GEOS- Chem model calculations, J. Geophys. Res., D30, 0. ID [] Yan, X., Shao, J., Chen, H., Shi, Y. (00). Geological environmental character of Lingang New City and its influences to the construction. Proceedings of East China geological survey achievements ( ), (In Chinese) [] Perissin, D., C. Prati, F. Rocca, D. R. Li and M. S. Liao (007), Multi-Track PS Analysis in Shanghai, paper presented at Envisat Symposium 007, Montreux, Switzerland. ID [] Li, X., Cheng, G.D., Liu, S.M., Xiao, Q., Ma, M.G., Jin, R., Che, T., Liu, Q.H., Wang, W.Z., Qi, Y., Wen, J.G., Li, H.Y., Zhu, G.F., Guo, J.W., Ran, Y.H., Wang, S.G., Zhu, Z.L., Zhou, J., Hu, X.L. and Xu, Z.W. (03) Heihe Watershed Allied Telemetry Experimental Research (HiWATER): scientific objectives and experimental design. Bulletin of the American Meteorological Society, doi: 0.75/BAMS-D ID [] Zhang, Y., Zhang, J., Wu H., et al. (0). Monito-ring of urban subsidence with SAR interferometric point target analysis. IJAEOG, Vol. 3, [] Wu, H., Zhang, Y., Chen, X., et al. (0). Ground deformation monitoring using small baseline DInSAR technique. CJG, Vol, 54, ID [] Mijling, B. and R.J. van der A, Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale, J. Geophys. Res., 7, 0. [] Air Quality Monitoring and Forecast Service on-line, bulletin/index.php. ID [] Corbari, C., Mancini, M., Li, J. and Su, Z. (03). Can satellite land surface temperature data be used similarly to ground discharge measurements for distributed hydrological model calibration?. Hydrol. Res. Submitted. [] Zhang, X., Liu, Y., Fang, Y., Liu, B. and Xia, D., Modeling and assessing hydrologic processes for historical and potential land-cover change in the Duoyingping watershed, southwest China. Physics and Chemistry of the Earth, Parts A/B/C(0). [] Cartus O, Santoro M, Schmullius C, Li Z., 0. Large area forest stem volume mapping in the boreal zone using synergy of ERS-/ tandem coherence and MODIS vegetation continuous fields. Remote Sensing of Environment, 5(3): [] Pang Yong, Huang Kebiao, Li Zengyuan, et al., 0. Forest Aboveground Biomass Analysis using Remote Sensing in the Greater Mekong Subregion, Resources Science, 33(0): [3] Santoro Maurizio, Christian Beer, Oliver Cartus et al., Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements. Remote Sensing of Environment, 5(): ID [] Jixian Zhang, Zheng Zhao, Guoman Huang and Zhong Lu, 0. CASMSAR: An Integrated Airborne SAR Mapping System, Phogrammetric Engineering & Remote Sensing, 78():0-4. [] Karjalainen, M., Kankare, V., Vastaranta, M., Holopainen, M., Hyyppä, J., 0. Prediction of Plot-Level Forest Variables Using TerraSAR-X Stereo SAR Data, Remote Sensing of Environment, 7(): ID [] Jianhua Gong, Yujuan Yue, (0). Impacts of the Wenchuan Earthquake on the Chaping River upstream channel change. International journal of remote sensing. [] Liu Q.,Wang Y., Chen L. 00. Impacts of aquatic environment protection oriented fishery on the structure of food web in Lake Qiandaohu, Acta Ecologica Sinica 30(0): ID. 067 [] Zhao Guoze et al. New experiments of CSELF electromagnetic method for earthquake monitoring. Chinese Journal of Geophysics, 00,53(3): [] Yaxin Bi, Shengli Wu, Pan Xiong, Xuhui Shen: A Comparative Analysis for Detecting Seismic Anomalies in Data Sequences of Outgoing Longwave Radiation. KSEM 009: ID [] Hooper, A., P. Segall, and H. Zebker (007), Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcan Alcedo, Galapagos, J. Geophys. Res.,, B07407, doi:0.09/006jb [] PuJianchen, Yao Tandong, Wang Ninglian, Ding Liangfu, Zhang Qihua. Puruogangri ice field and its variation since the Little Ice Age of the Northern Tibetan Plateau.Journal of Glaciology and Geocryology, vol. 4, no., pp.87-9, 00. (in Chinese) ID [] Lu, (006). The potential and challenge of remote sensing-based biomass ID [] Gao, Bo, Li Jia, Massimo Menenti, 0, The retrieval of land surface albedo (HJ- data) in rugged terrain. In: nd Int. Workshop Earth Observation and Remote Sensing Applications (EORSA 0), Shanghai, China, 8- June 0, pp4-45, doi: 0.09/EORSA (EI) [] Ghafarian H R, M Menenti, Li Jia & H den Ouden, 0. Reconstruction of cloud-free time series satellite observations of FY- land surface temperature. EARSeL eproceedings ISSN: , (): 3-3. ID [] Xu, X., et al., (008), Discovery of the Longriba Fault Zone in Eastern Bayan Har Block, China and its tectonic implication: Science in China Series-D: Earth Sciences, v. 5, no 9, p. 09-3, doi:0.007/s [] Jolivet, R., C. Lasserre, M.-P. Doin, S. Guillaso, G. Peltzer, R. Dailu, J. Sun, Z.-K. Shen and X. Xu, (0), Shallow creep on the Haiyuan Fault (Gansu, China) observed by SAR Interferometry, J. Geophys. Res., 7, B0640, doi:0.09/0jb ID [] Cheng, Y. C., Li, X. F., Xu, Q., Garcia-Pineda, O., Andersen, O. B. and Pichel, W. G. (0). SAR observation and model tracking of an oil spill event in coastal waters. Marine pollution bulletin 6(), [] Migliaccio, M., Nunziata, F., Brown, C., Holt, B., Li, X. F., Pichel, W. and Shimada, M. (0). Polarimetric synthetic aperture radar for transocean deepwater horizon oil accident monitoring. EOS 6(93), ID [] Ban, Y. and A. Jacob, 03. Object-based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ- Multispectral Data for Urban Land-Cover Mapping. IEEE Transaction on GeoScience and Remote Sensing, in press. [] P. Gamba, G. Lisini,03. Fast and efficient urban extent extraction using ASAR Wide Swath Mode data, IEEE J. of Selected Topics in Applied Earth Observation and Remote Sensing, in press. [3] Taubenböck H, Esch T, Felbier A, Wiesner M, Roth A & Dech S, 0. Monitoring of mega cities from space. Remote Sensing of Environment, vol. 7, pp ID [] Yang, B., Ma, S., Li, J., Liao, Y., Zhou, B., and C. Kuenzer, 0: Agricultural Drought Monitoring in Dongting Lake Basin by using of MODIS Data. Proceedings of the at Agro-Geoinformatics 0, August -4, Shanghai, China. [] Li, J., Liao, D., Yang, B., Zhou, B., and Kuenzer, C., 0: Comprehensive Evaluation of Ecosystem Health of East Dongting Lake Wetland Based on Remotely Sensed Images,.Proceedings of the at Agro-Geoinformatics 0, August -4, Shanghai, China. 76 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 77

40 DRAGON 3 LIST of istitutions list of istitutions DRAGON 3 Academy of Opto-Electronics, Chinese Academy of Sciences (CAS) Finnish Meteorological Institute (FMI) FINLAND Alfred Wegener Institute for Polar and Marine Research GERMANY Flemish Institute for Technological Research (VITO) BELGIUM Alterra, Wageningen University and Research Centre NETHERLANDS Free University of Berlin GERMANY Anhui Institute of Fine Mechanics and Optics (AIOFM),CAS Friedrich-Schiller-University Jena, Department for Earth Observation GERMANY ARGANS Ltd. UNITED KINGDOM Fudan University Beijing Institute of Technology HINA Gamma Remote Sensing SWITZERLAND Beijing Research Center for Information Technology in Agriculture Gansu Desert Control Research Institite(GDCRI) Carpathian Branch, Polish Geological Institute (PGI), National Research Institute POLAND GAUSS mbh/hochschule Bremen University of applied science GERMANY Cascades Volcano Observatory, U.S. Geological Survey USA German Aerospace Center (DLR) GERMANY Center for Earth Observation and Digital Earth (CEODE), CAS German Remote Sensing Data Centre, German Aerospace Centre (DFD, DLR) GERMANY Center for the Study of the Biosphere from Space (CESBIO) FRANCE Global Fire Monitoring Center (GFMC) GERMANY Centre for Energy and Resource Technology, Cranfield University UNITED KINGDOM GNSS research center, Wuhan University Centre for Terrestrial Carbon Dynamics (CTCD), University of Sheffield UNITED KINGDOM Guangdong University of Technology Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), University of Oxford UNITED KINGDOM Guilin University of Technology Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), Universtiy of Leeds UNITED KINGDOM Heidelberg University GERMANY China Aero Geophysical Survey & RS Center for Land and Resources Hohai University China Institute of Water Ressource and Hydropower Research (IWHR) Inner Mongolia Key Laboratory of Remote Sensing and GIS,Inner Mongolia Normal University Chinese Academy of Surveying and Mapping (CASM) Insitute of Geographic Sciences and Natural Reources, Chinese Academy Sciences Chinese Center for Diseases Control and Prevention (NIPD) Institut d Electronique et de Télécommunications de Rennes (IETR) FRANCE Cold and Arid Regions Environment and Engineering Research Institute (CAREERI), CAS Institut des Sciences de la Terre FRANCE Collecte Localisations Satellites (CLS) FRANCE Institut f. Physik d. Atmosphaere, Deutsches Zentrum f. Luft- u. Raumfahrt (DLR) GERMANY College of Global Change and Earth System Science, Beijing Normal University Institut Français de Recherche pour l Exploitation de la Mer (IFREMER) FRANCE College of Marine Sciences, Shanghai Ocean University (SHOU) Institut of Remote Sensing Applications, CAS College of Resource and Environment Science, Hunan Normal University (HUNNU) Institute for Climate and Global Change Research, Nanjing University College of Resources Science and Technology, Beijing Normal University Institute for Electromagnetic Sensing of the Environment (IREA-CNR) ITALY Deimos Imaging SPAIN Institute of Agricultural Resources and Regional Planning (IARRP),Chinese Academy of Agricultural Sciences(CAAS) DEOS-Delft University of Technology NETHERLANDS Institute of Applied and Computational Mathematics, FORTH GREECE Department of Civil and Structural Engineering, the Hong Kong Polytechnic University Institute of Applied Physics (IFAC-CNR) ITALY Department of Computer Science, Systems and Production, Tor Vergata University ITALY Institute of Crustal Dynamics(ICD), China Earthquake Administration (CEA) Department of Forestry Management, Beijing Forestry University Institute of Desertification Studies,CAF Department of Geography and Regional Research, University of Vienna AUSTRIA Institute of Electronics(IECAS), CAS Department of Geophysics, Peking University Institute of Forest Resources Information Techniques(IFRIT), the Chinese Academy of Forestry (CAF) Department of Physics and Astronomy, University of Leicester UNITED KINGDOM Institute of Geodesy and Geophysics(IGG), CAS Department of Physics, University of Athens GREECE Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig GERMANY Dept. of Remote Sensing and Landscape Information Systems, University of Freiburg GERMANY Institute of Geodesy, University of Stuttgart GERMANY Dipartimento di Elettronica ed Informazione, Politecnico di Milano (POLIMI) ITALY Institute of Geography and Lake, CAS Dipartimento di Elettronica, Università di Pavia ITALY Institute of Geology(IG),China Earthquake Administration (CEA) Dipartimento di Ingegneria Agraria ed Agronomia del Territorio, University of Naples Federico II ITALY Institute of Geomatics and Analysis of Risk (IGAR) SWITZERLAND Dipartimento di Ingegneria Idraulica ed Applicazioni Ambientali, Università di Palermo ITALY Institute of Geophysics, China Earthquake Administration Dipartimento di Ingegneria Idraulica, Ambientale, delle Infrastrutture Viarie e del Rilevamento, Politecnico di Milano (DIIAR POLIMI) ITALY Institute of Intelligent Information Processing, Xidian University(XDU) Dipartimento di Scienze e Tecnologie per l Agricoltura, University of Tuscia ITALY Institute of Marine Sciences (ISMAR-CNR) ITALY Dipartimento per le Tecnologie, Università di Napoli Pathenope ITALY Institute of Methodologies of Environmental Analysis (IMAA-CNR) ITALY Estacion Experimental de Zonas Aridas (CSIC) SPAIN Institute of Photogrammetry & Remote Sensing, Vienna University of Technology (IPF, TU Wien) AUSTRIA ETH Zurich, Institute of Environmental Engineering SWITZERLAND Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University Finnish Geodetic Institute FINLAND Institute of Remote Sensing Applications(IRSA), CAS 78 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 79

41 DRAGON 3 LIST of istitutions list of istitutions DRAGON 3 Institute of RS and GIS, School of Earth and Space Sciences, Peking University Shanghai Jiao Tong University Institute of Satellite Navigations & Spatial Information System,Hohai University Shanghai Ocean University Institute of Space and Earth Information Science(ISEIS), the Chinese University of Hong Kong (CUHK) South China Sea Institute of Oceanology(SCIO), CAS Institute of Space Sciences, Freie Universitat Berlin GERMANY Space Research Institute, Austrian Academy of Sciences AUSTRIA Institute of Tebetan Plateau Research(ITR), CAS State Key Lab. of Info. Eng. in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University Institution College of Resource Enviroment and Tourism,Capital Normal University State Key Laboratory of Estuarine and Coastal Research(SKLEC), East China Normal University (ECNU) IsardSAT S.L SPAIN Technical University Dresden GERMANY Istituto Nazionale di Geofisica e Vulcanologia (INGV) ITALY Technical University Munich GERMANY ITC, Faculty of the University of Twente NETHERLANDS Telecom ParisTech FRANCE IUII, University of Alicante SPAIN The First Institute of Oceanography(FIO), SOA Julich Research Centre GERMANY The Institute of Tibetan Plateau Research(ITP), CAS Key Laboratory of Virtual Geographic Environments, Nanjing Normal University The Second Institute of Oceanography(SIO), SOA Laboratoire de Géologie de l Ecole Normale Supérieure (LG ENS) FRANCE Tsinghua University Laboratoire d Oceanographie de Vilelfranche (LOV, CNRS/UPMC) FRANCE Universitat de Valencia SPAIN Laboratory of Microwave Remote Sensing, National Space Science Center (NSSC), CAS Universite Catholique de Louvain BELGIUM Meteo France / CNRS FRANCE Université de Savoie (LISTIC) FRANCE Mullard Space Science, Lab., Dept. of Space and Climate Physics, University College London (MSSL, UCL) UNITED KINGDOM Université de Strasbourg FRANCE Nanjing Institute of Geography and Limnology, CAS Université de Strasbourg (LSIIT) FRANCE Nanjing University Université Pierre et Marie Curie (LATMOS) FRANCE Nanjing University of Information Science and Technology University of Electronic Science and Technology of China Nansen Environmental and Remote Sensing Center (NERSC) NORWAY University of Extremadura, Escuela Politécnica de Cáceres SPAIN National Meteorological Center (NMC) University of Hamburg GERMANY National Observatory of Athens (NOA) GREECE University of Hamburg Centre for Marine and Atmospheric Sciences GERMANY National Oceanography Centre, University of Southampton UNITED KINGDOM University of Helsinki FINLAND National Satellite Meteorological Center(NSMC) University of Iceland ICELAND National Space Science Center (NSSC, renamed from CSSAR), CAS University of Siena ITALY Nederlands Instituut voor Ecologie (NIOO-KNAW) NETHERLANDS University of Stuttgart GERMANY Ocean Remote Sensing Institute(ORSI), Ocean University of China (OUC) University of Valencia SPAIN Prediction Center of East China Sea, State Oceanic Administration (SOA) University of Valladolid (LATUV) SPAIN Remote Sensing Application Consultants Ltd. (RSAC) UNITED KINGDOM University of Wuerzburg GERMANY Remote Sensing Technology Application Centre, IWHR Wageningen University NETHERLANDS Remote Sensing Technology Institute, German Aerospace Center (IMF, DLR) GERMANY Xiamen University Royal Institute of Technology (KTH) SWEDEN Yantai Institute of Coastal Zone Research(YIC), CAS Royal Netherlands Meteorological Institute (KNMI) NETHERLANDS Zhejiang Center for Applications of Geoinformatics School and Observatory of Earth Sciences, (CNRS-IPGS ) University of Strasbourg FRANCE Zhejiang University School of Computing and Mathematics, University of Ulster UNITED KINGDOM School of Earth, Ocean & Environmental Sciences, University of Plymouth UNITED KINGDOM School of Geodesy and Geomatics, Wuhan University School of Geographical and Earth Sciences, University of Glasgow UNITED KINGDOM School of Geography and Remote Sensing Sciences, Beijing Normal University School of Geosciences, University of Edinburgh UNITED KINGDOM School of Land Science and Technology, China University of Geosciences in Beijing School of Resources and Environment Science, East China Normal University SERTIT, University of Strasbourg FRANCE Shanghai Institute of Optics and Mechanics (SIOM) 80 THE 03 DRAGON 3 BROCHURE THE 03 DRAGON 3 BROCHURE 8

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