GEO Joint Experiment for Crop Assessment and Monitoring (JECAM): 2014 Site Progress Report

Similar documents
Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics

Time and resolution : COSMO-SkyMed VHR data in support to precision farming applications

GI Technology for Disaster Management

RADAR Remote Sensing Application Examples

The Relationship between Vegetation Changes and Cut-offs in the Lower Yellow River Based on Satellite and Ground Data

DROUGHT RISK EVALUATION USING REMOTE SENSING AND GIS : A CASE STUDY IN LOP BURI PROVINCE

Use of SAR data for Rice Assessment

Ganbat.B, Agro meteorology Section

Improved sea-ice monitoring for the Baltic Sea Project summary

EXTRACTION OF REMOTE SENSING INFORMATION OF BANANA UNDER SUPPORT OF 3S TECHNOLOGY IN GUANGXI PROVINCE

Crop and pasture monitoring in Eritrea

SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO

Introduction to Satellite Derived Vegetation Indices

DUAL-POLARIZED COSMO SKYMED SAR DATA TO OBSERVE METALLIC TARGETS AT SEA

Sentinel-1 Long Duration Mutual Interference

RESEARCH METHODOLOGY

Assimilating terrestrial remote sensing data into carbon models: Some issues

LAND COVER CLASSIFICATION BASED ON SAR DATA IN SOUTHEAST CHINA

Shashi Kumar. Indian Institute of Remote Sensing. (Indian Space Research Organisation)

THE DESIGN AND IMPLEMENTATION OF SUGAR-CANE INTELLIGENCE EXPERT SYSTEM BASED ON EOS/MODIS DATA INFERENCE MODEL

SEASONAL RAINFALL FORECAST FOR ZIMBABWE. 28 August 2017 THE ZIMBABWE NATIONAL CLIMATE OUTLOOK FORUM

4. Verification and evaluation (monitoring) 4.1 Verification using visual information

GIS AND REMOTE SENSING FOR WATER RESOURCE MANAGEMENT

Monitoring Sea Ice with Space-borne Synthetic Aperture Radar

PROJECT REPORT (ASL 720) CLOUD CLASSIFICATION

REMOTELY SENSED INFORMATION FOR CROP MONITORING AND FOOD SECURITY

J2.6 SONAR MEASUREMENTS IN THE GULF STREAM FRONT ON THE SOUTHEAST FLORIDA SHELF COORDINATED WITH TERRASAR-X SATELLITE OVERPASSES

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017)

FLOOD DAMAGE ASSESSMENT INTEGRATING GEOSPATIAL TECHNOLOGIES. A CASE STUDY IN HUE, VIET NAM

Rating of soil heterogeneity using by satellite images

West meets East: Monitoring and modeling urbanization in China Land Cover-Land Use Change Program Science Team Meeting April 3, 2012

Effect of rainfall and temperature on rice yield in Puri district of Odisha in India

Permafrost: Earth Observation Applications: Introduction

Crops Planting Information Retrieval at Farmland Plot Scale Using Multi-Sources Satellite Data

EXTRACTION OF FLOODED AREAS DUE THE 2015 KANTO-TOHOKU HEAVY RAINFALL IN JAPAN USING PALSAR-2 IMAGES

Weather & Climate of Virginia

Geoscience Australia Report on Cal/Val Activities

Land Surface Remote Sensing II

ARUBA CLIMATOLOGICAL SUMMARY 2014 PRECIPITATION

VISUALIZATION URBAN SPATIAL GROWTH OF DESERT CITIES FROM SATELLITE IMAGERY: A PRELIMINARY STUDY

Indian National (Weather) SATellites for Agrometeorological Applications

Changing Hydrology under a Changing Climate for a Coastal Plain Watershed

Detecting an area affected by forest fires using ALOS PALSAR

Possible Applications of Deep Neural Networks in Climate and Weather. David M. Hall Assistant Research Professor Dept. Computer Science, CU Boulder

Probability models for weekly rainfall at Thrissur

Satellite Remote Sensing for Ocean

Floods in Sudan, August 2016

Project Name: Implementation of Drought Early-Warning System over IRAN (DESIR)

The Study of Dynamic Monitor of Rice Drought in Jiangxi Province with Remote Sensing

Comparison between Multitemporal and Polarimetric SAR Data for Land Cover Classification

Monthly Overview. Rainfall

Global Satellite Products & Services for Agricultural and Vegetation Health

ANALYSIS AND VALIDATION OF A METHODOLOGY TO EVALUATE LAND COVER CHANGE IN THE MEDITERRANEAN BASIN USING MULTITEMPORAL MODIS DATA

over the next three weeks could lower this estimate significantly. Near perfect conditions are needed to realize this projected yield.

Climate Change Impact on Air Temperature, Daily Temperature Range, Growing Degree Days, and Spring and Fall Frost Dates In Nebraska

ZRCSAZU. Remote sensing and Earth observation data at ZRC SAZU. dr. Tatjana Veljanovski Atrij ZRC Ljubljana

Will a warmer world change Queensland s rainfall?

Interannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region

THE STUDY OF NUMBERS AND INTENSITY OF TROPICAL CYCLONE MOVING TOWARD THE UPPER PART OF THAILAND

DROUGHT ASSESSMENT USING SATELLITE DERIVED METEOROLOGICAL PARAMETERS AND NDVI IN POTOHAR REGION

Workshop on Drought and Extreme Temperatures: Preparedness and Management for Sustainable Agriculture, Forestry and Fishery

Hong Kong Meteorological Society 香港氣象學會.

FLOOD ANALYSIS USING SATELLITE DATA AND GEOMORPHOLOGICAL SURVEY MAP SHOWING CLASSIFICATION OF FLOOD-INUNDATED AREAS

Land cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan.

Land Surface Temperature Measurements From the Split Window Channels of the NOAA 7 Advanced Very High Resolution Radiometer John C.

Meteorology. Chapter 15 Worksheet 1

Using COSMO-SkyMed images to improve river flood monitoring and forecasting.

Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas

Monsoons: A Three Season Play. Kathy Sundstedt School not available. Content Area (Req.): Physical Geography, English/Reading

SAR Coordination for Snow Products

2015: A YEAR IN REVIEW F.S. ANSLOW

Siaga antelope migration revisited: relationship to recent mass deaths

Improvement of Hazard Assessment and Management in the Philippines

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (May 2017)

RESEARCH METHODOLOGY

DLR s TerraSAR-X contributes to international fleet of radar satellites to map the Arctic and Antarctica

Director: Soroosh Sorooshian

Introduction to African weather

Meteorological Drought Classification Using Normalized Difference Vegetation Index

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017)

PROJECTING THE SOLAR RADIATION IN NASARAWA-NIGERIA USING REITVELD EQUATION

New NASA Ocean Observations and Coastal Applications

A NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) TIME-SERIES OF IDLE AGRICULTURE LANDS: A PRELIMINARY STUDY

Application of remote sensing for agricultural disasters

Fjernmåling og modellering av oljesøl - på åpen sjø og i is

Remote Sensing and EO activities at the University of Turku

Vegetation Remote Sensing

Weather and climate outlooks for crop estimates

ESCI 1010 Lab 7 Hurricanes (AKA: Typhoons, Cyclones)

KNOWLEDGE-BASED CLASSIFICATION OF LAND COVER FOR THE QUALITY ASSESSEMENT OF GIS DATABASE. Israel -

East Africa The 2015 Season (Long Rains)

Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015)

Applications of yield monitoring systems and agricultural statistics in agricultural (re)insurance

West and East Africa The 2014 Rainfall Season

Operational ice charting in mid-latitudes using Near-Real-Time SAR imagery

SEPTEMBER 2013 REVIEW

Greening of Arctic: Knowledge and Uncertainties

Colorado State University, Fort Collins, CO Weather Station Monthly Summary Report

Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC

Thai Meteorological Department, Ministry of Digital Economy and Society

Transcription:

GEO Joint Experiment for Crop Assessment and Monitoring (JECAM): JECAM Test Site Name: China - Guangdong 2014 Site Progress Report Team Leader and Members: Prof Wu Bingfang (Leader), Jiratiwan Kruasilp, Zheng Yang, Zhang Miao, Zhang Ning, Zeng Hongwei, Zou Wentao. No report was received for the JECAM site in Guangdong province at Xuwen. However, a report was received for a new site in development in Guangdong province at Taishan, and that report is reproduced here. Project Objectives The original project objectives have not changed. They are: Crop identification and Crop Area Estimation o Object-based Image Analysis and The Support Vector Machine (SVM) classification method o The fusion of SAR and Optical satellite data o Statistical analysis Crop Condition/Stress o NDVI o The ground truth information Yield Prediction and Forecasting o Artificial Neural networks (NN) method Phenological Events and Estimation of Rice biophysical variables o Multiple regression analysis o Leaf Area Index (LAI) measured with hemispherical lens. Site Description Location Upper Left: 22 41'14.05"N, 442 22'12.54"E, Upper Right: 22 41'11.50"N, 441 2'05.55"E Lower Left: 24 14'24.01"N, 442 21'04.44"E, Lower Right: 24 12'20.55"N, 441 4'15.54"E Topography Coastal plain and Mountain

Soils Soils in the study site are mainly lateritic red soil. Drainage class/irrigation Most of the rice cultivated areas have an irrigation system. Crop calendar The predominant crop in the study area is rice, with some sugarcane and vegetables. Sowing of main rice takes place in late March and June, to be harveste d in July. The second rice crop is sown in August and is harvested from late October to November. Field size The size of the rice plots is 5 to 15 hectares. Figure 1: Photos of Taishan (Guangdong) Site

Climate and weather The climate is characterized by relatively high temperatures. The average temperature is 22 o Celsius and the warmest month is July with an average temperature of 28 o Celsius. The coolest month is January, with an average temperature of 14 o Celsius. The rainfall is evenly distributed over the entire year. In summer, the rainfall comes from tropical cyclones. The average amount of rainfall is 2402.8 mm. EO Data Received/Used COSMO-SkyMed : Supplier: Agenzia Spaziale Italiana, ASI SAR 12 scenes April to November 2013 Beam modes/ incidence angles/ spatial resolutions: Wide region/ 22.73 degree/ 30 meters SARscape module has been used to process the Wide region mode of the satellite. RADARSAT-2: Supplier: MDA/CSA SAR 6 scenes March to November 2013 Beam modes/ incidence angles/ spatial resolutions: Fine Quad mode/ FQ10 (29.1 30.9 degrees)/ 8 meters The data sometimes are cancelled if there are the high priority orders. Figure 2: COSMO-SkyMed Image of Taishan Site

Figure 3: RADARSAT-2 Image of Taishan Site RapidEye: MDA Optical 4 scenes June to October 2013 Level 1B It is very difficult to acquire optical satellite imagery because the sky is almost always covered by cloud. In situ Data Field measurement of rice parameters were conducted in parallel to the Satellite data acquisition. The main variables and methods are shown in Table 1. The principle rice cropping systems in the study site is the double rice crop. Sowing of main rice takes place in late March and June, to be harvested in July. The second rice crop is sown in August and is harvested from late October to November. Thus, all the variables were measured once a month from April to November in 2013. Since the study area is located in the coastal area, the sky is almost always covered by cloud. The rainfall is evenly distributed over the entire year. In 2013, a severe tropical storm

intensified into a typhoon and lashed the area with heavy rain. Hence, the water level in the rice field is higher than the normal situation. Figure 4: RapidEye Image of Taishan Site Table 1: In situ Methods at Taishan Site Variables Equipment/Methods LAI LAI 2000 and Hemispherical lens / The digital hemispherical photos analyzed using the CANEYE image analysis. Fractional of vegetation cover Rice variety and transplanting date Biomass Yield Density/canopy height Number of bunch Water level in the field Hemispherical lens/ The digital hemispherical photos analyzed using the CANEYE image analysis. Interview Oven dried and weight Oven dried and weight Tape measured / Count Tape measured / Count Tape measured

Figure 5: In situ Measurements, Taishan Site Collaboration We did not collaborate with other JECAM sites. However, we have participated in the research team from the Geo-informatics and Space Technology Development Agency (Public Organization): GISTDA, Thailand. We proposed that Thai and Chinese researchers jointly perform field survey in both Thailand and China s study. The research shall apply crop condition and production monitoring technology for both study sites. Results The correlation between the RADARSAT-2 backscatter coefficient and biophysical parameters is shown in Table 2.

Table 2: Correlation between RADARSAT-2 Backscatter Coefficient and Biophysical Parameters Polarization Plant height Days after transplanting Leaf area index Biomass HH 0.7810 0.6832 0.6609 0.8403 HV 0.5295 0.4614 0.4371 0.5115 VH 0.5806 0.5055 0.4772 0.4335 VV 0.3934 0.2793 0.2515 0.5901 HH/VV 0.7132 0.7814 0.7793 0.7827 The correlation between backscattering coefficient (db) and LAI is shown in Figure 6. Figure 6: Correlation between Backscatter Coefficient and LAI The RADASAT-2 Quad-polarization data provides the four polarizations HH, HV, VH and VV. Generally, HH-polarization shows the highest stepwise change of all four polarizations. The stepwise change in HV/VH-polarization was greater than VV-polarization. It can be seen from the table that HV and VH polarization has very similar backscattering and is the lowest backscattering value. The correlation between VV-polarization and rice crop age is the lowest. Experience with the COVE Planning Tool We did not use the COVE Planning Tool to order Satellite data. Nonetheless, the tool is quite straightforward to search and preview the information that users are looking for, while an error occurred after addition of multiple products to the Cart. We definitely would like to participate in a short training course to gain an understanding of the functions of the COVE Planning Tool.

Plans for Next Growing Season We hope to carry out field experiments synchronous with some satellites through the study site in 2014. We will order the same type of SAR data and additional optical imagery such as MODIS and Landsat-8. Publications Not yet. For other GEO JECAM site reports or to view summaries and background information please see the 2014 Progress Report that can be found on the annual reports page on the JECAM website here: http://www.jecam.org/?/charter/annual-reports