Snow maps based on satellite data

Transcription

1 Snow maps based on satellite data SNAPS Work Package 4: Final report on WP 4 Prepared by Eirik Malnes, Norut, Norway Kari Luojus, Jaakko Ikonen and Matias Takala, FMI, Finland Hrobjartur Thorsteinsson and Thorsteinn Thorsteinsson, IMO, Iceland Issue / Revision: 1 / 0

2 Document controlled by: Harpa Grímsdóttir Page ii

3 SNAPS Report GRANT AGREEMENT NR: SUBJECT: SNAPS WP 4 PROJECT COORDINATOR: IMO ISSUE / REVISON: 1 / 0 CONTRACTOR S REF: WP 4 final report ABSTRACT: WP 4 focused mainly on the adaptation of tried and tested satellite based snow cover monitoring systems, intensively developed by Norut and FMI, applied to target regions in the participating countries. All near-real-time satellite snow cover processing chains have now been developed and are producing maps that are immediately made available online to the public. The satellite based snow maps will serve the snow and avalanche forecasting work packages by providing mapped snow observations useful for validating, tuning and constraining meteorological and surface snow pack modeling. This project was funded by EU Interreg, Northern Periphery Programme AUTHORS: MALNES, E., LUOJUS, K.,IKONEN J., TAKALA M., THORSTEINSSON, H. & THORSTEINSSON, T. Page iii

4 DOCUMENT CHANGE LOG Issue/ Revision Date Modification Modified pages Observations Initializing doc All E.Malnes Nearly completed doc All E.Malnes /05/13 Chapter H.Thorsteinsson 1 24/05/13 Style and typos All H.Thorsteinsson June Typos and grammar All Harpa Grímsdóttir July Final document styling, typos and grammar All Sigurlaug Gunnlaugsdóttir Page iv

5 Table of Contents 1. Introduction Purpose of the document Outline Acronyms SAR activities (Norut) Integration of auxiliary data Building a SAR reference data base Adaptation of the system to variable satellite orbits Processing of historical data for target areas Radarsat-2 based wet snow monitoring Near real time monitoring Current status and on-going issues Snow Water Equivalent (SWE) maps for SNAPS target areas (FMI) An SWE processing chain for Iceland The retrieval methodology System level implementation Current status and on-going issues Reflectance snow cover maps for Iceland (IMO) Methodology System development history The NDSI calibration Current status and on-going issues Data sharing and presentation Data archive Image player Conclusions Recommendations for further work References Page v

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7 1. Introduction This document gives a final report on work package 4 of the Northern Periphery Project, SNAPS. The SNAPS project focuses on snow and avalanche services for transport infrastructure in selected areas within the northern periphery in Iceland, Norway, Sweden and Finland. Near-real time snow cover maps have been made available online to the public in the target areas and they will be further developed to become an input to snowdrift and avalanche forecasts aimed at transport authorities. The SNAPS project will help to improve safety and efficiency of public transport in the target areas. This will help to increase competitiveness as well as sustainability of the communities. All products and services will continue to run after the lifetime of this project. The products will also be applicable to larger areas in the northern periphery and to other snow related fields, such as hydropower and avalanche forecasting over villages and ski areas. Cross-fertilization of technology, infrastructure and the sharing of know-how between the participating institutes is expected to provide broader improvements to their public services Purpose of the document The main purpose of this document is to give a final report on the work performed in work package 4 of the NPP project, SNAPS. Title: WP 4 Snow maps for target regions in the Northern Periphery. Strategic Focus: Adaptation and transfer of Norwegian and Finnish snow mapping services that are based on remote sensing to other target regions in the Northern Periphery. Expected outcome: Near-real time production of snow cover maps for the target regions. The maps will serve the snow and avalanche forecasting work packages by providing mapped snow observations useful in validation, tuning and constraining meteorological and snow pack models. Furthermore the SNASP snow products are likely to be useful in various other fields and serve as a valuable record of past snow conditions. WP 4 focused mainly on the adaptation of tried and tested operational snow cover monitoring systems, intensively developed by Norut and FMI, applied to target regions in the participating countries. Since at the onset of this project no targeted satellite-based snow mapping service existed for Iceland, special emphasis has been made on the Vestfirðir region as a key testing ground for further development of the snow map services. Snow mapping will serve the snow and avalanche forecasting work packages and aid tourism and long-term planning for infrastructure build up in regions within the northern periphery Outline Chapter 2 gives an overview of the activities performed by Norut in relation to using Synthetic Aperture Radar (SAR) to detect wet snow. Page 3

8 Chapter 3 gives an overview of FMI s work related to Snow Water Equivalent retrieval using passive microwave sensors. Chapter 4 gives an overview of IMO s activities related to MODIS snow cover mapping. Chapter 5 describes work related to setting up a centralized archive and viewing functionality for snow maps in a web-browser. Chapter 6 gives the conclusions and recommendations for further work Acronyms AMSR-E ASAR CSV DEM Envisat ESA FMI GeoTIFF IMO MODIS NASA NDSI NORUT NPP NRT SAR SD SCDA SSM/I SWE WP WSM Advanced Microwave Scanning Radiometer Advanced Synthetic Aperture Radar Comma Separated Value Digital Elevation Model Environmental Satellite (ESA) European Space Agency Finnish Meteorological Institute Geographic Tagged Image File Format Icelandic Meteorological Office Moderate Resolution Imaging Spectrometer National Aeronautics and Space Administration Normalized Difference Snow Index Northern Research Institute, Norway Northern Periphery Programme Near-Real Time Synthetic Aperture Radar Snow Depth Simple Cloud Detection Algorithm Special Sensor Microwave Imager Snow Water Equivalent Work Package Wide Swath Mode Page 4

9 2. SAR activities (Norut) The main focus for Norut has been to adapt existing satellite radar based snow cover monitoring systems developed by Norut to target regions in the participating countries. Since no targeted satellite-based snow mapping services have existed for Iceland, this WP has made special emphasis on the Vestfirðir (Westfjords) region of Iceland as a key testing ground for further development of the methodology Integration of auxiliary data A digital elevation model from Iceland has been prepared in appropriate projections. It was decided to use the Corine land cover data set as a vegetation mask for all regions (see figure 2.1 for examples from Vestfirðir and Stryn). The digital elevation models are required in order to pre-process the Synthetic Aperture Radar (SAR) data (geocoding). DEM s for all defined target areas were prepared as input files to the SAR processing system, and are currently in use for the processing of historical data (T.4.4). Figure 2.1. Corine land cover map for the defined test areas in Vestfirðir, Iceland and Stryn, Norway Building a SAR reference data base The method used to detect wet snow with C-band SAR is based on Nagler and Rott (2000) with adaptations provided by Storvold et al. (2005). The method depends on the availability of reference data with similar satellite image geometry (repeated pass) for a period when the snow is not wet. If σ ref is the reference backscatter for a pixel and σ 0 is the current SAR measurement, wet snow is detected when: σ 0 - σ ref < -3 db. Norut has developed a method to calculate a reference SAR image database for a region. This method is based on collecting SAR images over an extensive period, and calculating the average image for the period when the snow is generally not wet. This period is usually estimated from July 1 to April 1. Page 5

10 Norut had a large collection of Envisat ASAR WSM images for 3 of the 4 target regions before the SNAPS project started, however the Iceland region was new. The first task by Norut was to set up procedures for collecting data for this new region. A total of 1053 data sets were collected in the period until April 2012 for Vestfirðir. A similar number of data sets were also available for the 3 other target regions. A system for archiving and indexing the images was developed together with improving the capabilities of geocoding such a large number of SAR images under the Norut GSAR framework. A system was subsequently implemented to temporally filter the data set to remove scenes with wet snow from the database. This was done before the final step where all data from all relevant satellite orbits were averaged in order to obtain the best mean reference data archive for the target area. Reference databases for all four target regions were subsequently calculated for all orbits and stored. Figure 2.2. Reference data sets from Vestfirðir (left) and Stryn (right). The number of SAR scenes (lower panels) used for calculating the average reference background SAR image varies slightly but is typically of the order Adaptation of the system to variable satellite orbits Envisat changed its repeat pass orbit in October 2010 into a new orbit that slowly drifts out of its repeat pass pattern. This had the consequence that reference data acquired before this date could not be directly used. In order to allow use of the reference data, Norut developed a system that calculates the closed orbit between the current satellite orbit and the old orbit in order to have a similar incidence angle. This system was developed and tested in the SNAPS project and is now used in the processing of historical data Processing of historical data for target areas The initial time-series of historical data sets for each of the four target regions have been processed. The time series provide daily interpolated wet snow maps for each of the regions from the start of the data set ( February 2007 for Iceland, June 2005 for Norway and Sweden) until the sudden end of life-time for Envisat (April 2012). Page 6

11 Figure 2.3. Examples of wet snow maps for Lyngen (upper left), Sweden (upper right), Vestfirðir (lower left) and Stryn (lower right) : Wet snow from SAR (red) has been superimposed on a MODIS snow cover fraction map. Note: The Vestfirðir maps have a projection error (the blue ocean color creeping onto land surface) that was not fixed during developments with the Envisat data. This problem has been fixed in the near real-time snow maps using Radarsat-2. The raw snow wetness datasets were unaffected Radarsat-2 based wet snow monitoring Due to the loss of Envisat ASAR in April 2012, an initial investigation was performed to find other sources of SAR data for the SNAPS project. Due to the high costs of other data sources (e.g. TerraSAR-X and Cosmo-Skymed) it was soon realised that the only viable data source would be granted via the Norwegian Radarsat-2 agreement, which Norut has access to as a non commercial research institute. Since May 2012, Norut has systematically downloaded data over all the four target regions in SNAPS. The availability is somewhat limited. However, the next generation SAR satellite from ESA is likely to provide great improvements to current data availability Near real time monitoring From January 2013 a near-real time processing system has been running at Norut that: Downloads Radarsat-2 data from KSAT Processes data into wet snow maps for the 4 target regions Uploads wet snow maps to the SNAPS server at FMI which is in turn is uploaded to the SNAPS viewer on the SNAPS website. Examples of snow maps from the 4 regions are provided in figure 2.4. Page 7

12 Figure 2.4. Example snow maps: Vestfirðir , Stryn , Sweden , Lyngen: Current status and on-going issues The Norut wet snow detection was successfully transferred to the target regions, and technical issues with variable satellite orbital perspective were resolved. The sudden death of Envisat meant that the processing was changed over to a different SAR instrument (on Radarsat-2). This caused temporary problems and delay in commissioning the mapping service in NRT. The Vestfirðir region is currently affected by much fewer SAR image acquisitions than were observed with Envisat. However, the next generation SAR satellite from ESA is likely to provide great improvements to future SAR data availability. Page 8

13 3. Snow Water Equivalent (SWE) maps for SNAPS target areas (FMI) The goal of Finnish Meteorological Institute (FMI) in the SNAPS project was to adapt the FMI developed snow water equivalent (SWE) mapping technology: Firstly to extend the spatial coverage of the near-real time (NRT) SWE monitoring service to previously unmapped region of Iceland and secondly to augment the range of utilized input data to additional ground-based weather station data from Iceland. The successful implementation of the above two items would enable FMI to produce daily maps of SWE covering the region of Iceland. The SWE mapping methodology developed by FMI has previously been operated to produce maps for large parts of the northern hemisphere, excluding mountains, glacier, Greenland and Iceland. Figure 3.1. Snow water equivalent map, prior EC SNAPS project, covering Northern Hemisphere for 15 February 2011, excluding Greenland, mountains, glaciers and Iceland An SWE processing chain for Iceland Within the SNAPS project, FMI and IMO were successfully able to augment the utilization of the Icelandic weather station data into the SWE processing chain and extent the spatial coverage of SWE monitoring to cover Iceland. The current processing chain provides maximum likelihood estimates of SWE for Iceland based on assimilation of satellite-based passive microwave radiometer data and snow depth measurements by the Icelandic synoptic weather stations. An automatic processing chain was implemented by FMI to process the SWE data on a daily basis, with data dissemination system implemented by IMO. Page 9

14 Figure 3.2. Snow water equivalent map for Iceland for 23 January The retrieval methodology The processing system applies passive microwave observations and weather station observations collected by IMO in an assimilation scheme to produce maps covering all of Iceland. A semi-empirical snow emission model is used for interpreting the passive microwave (radiometer) observations through model inversion to the corresponding SWE estimates. The SWE retrieval methodology in Pulliainen (2006) is complemented with a time-series melt-detection algorithm (Takala et al. 2009). The two algorithms are combined to produce snow water equivalent maps incorporated with information on the extent of snow on coarse resolution (25 km 25 km) grid cells (Takala et al. 2011). The SWE estimates are complemented with uncertainty information on a grid-cell level. Estimates of Snow Depth (SD) based on emission model inversion of two frequencies, 18.7 and 36.5 GHz, are first calibrated over EASE-Grid cells with weather station measurements where SD is available. Snow grain size is used in the model as a scalable model input parameter (being determined from the input radiometer and weather station data). These values of grain size are used to construct a kriging-interpolated background map of the effective grain size, including an estimate of the effective grain size error. The map is then used as input in model inversion over the full spatial coverage of available radiometer observations, providing an estimate of the SD on a snow map. In the inversion process, the effective grain size in each grid cell is weighed with its respective error estimate. A snow density value is applied to each grid cell to connect SD to SWE. Areas of wet snow are masked according to observed brightness temperature values using an empirical equation, as model inversion of SD/SWE over areas of wet snow is not feasible due to the saturated brightness temperature response. The weather station observations of SD are further interpolated to provide a crude estimate of the SD (or SWE) background. The SWE estimate map and SD map from weather station observations are combined using a Bayesian spatial assimilation approach to provide the final SWE estimates. The snow emission model applied is the semi-empirical HUT snow emission model (Pulliainen et al., 1999). The model calculates the brightness temperature from a single layer homogenous snowpack covering frozen ground in the frequency range of 11 to 94 GHz. Input parameters of the model Page 10

15 include snowpack depth, density, effective grain size, snow volumetric moisture and temperature. Separate modules account for ground emission and the effect of vegetation and atmosphere. The detection of the snow extent is based on a time-series melt detection approach described in (Takala et al. 2009). The algorithm can be used to determine the onset of the snowmelt season using the available radiometer observations on a hemispherical scale covering the product time-series up to present day. The methodology has been calibrated against a vast pan-arctic dataset covering most of the land areas of northern Eurasia between the years 1979 to The areas that are identified as snow covered with the melt detection algorithm, but for which a SWE estimate is not produced, are given a marginal SWE value (0.001 mm) in the final SWE product. This information can be used to determine the extent of snow cover. The areas with a SWE value of 0 mm are bare ground, and areas with SWE of mm or above are snow covered. Figure 3.3. Flowchart of the SWE processing and retrieval chain (Takala et al. 2011) System level implementation There are four primary steps to the retrieval scheme: Estimating the snow depth field: SD observations from synoptic weather stations are obtained for the northern hemisphere from ECMWF. The stations located in mountainous areas are filtered out, as are the deepest 1.5% of reported snow depth values in order to avoid spurious or erroneous deep snow observations. Once this filtering is performed, an observed SD field is produced from the synoptic weather station observations by ordinary kriging interpolation to 5 km spatial resolution. Page 11

16 Forward modelling of brightness temperature: The available synoptic weather station measurements of SD are used as input to a forward modeling simulation of brightness temperature (T B ) using the single-layer HUT snow emission model. Additionally, the approach takes into account atmospheric effects to space-borne observed T B. The model is fit to spaceborne observed T B values at the locations of the weather stations by optimizing the value of effective snow grain size. Estimating effective snow grain size: A spatially continuous background field of the effective snow grain size (including a variance field) is interpolated by kriging from the snow grain size estimates, produced at weather station locations, derived in the previous step Assimilating snow depth: A map of spatially continuous assimilated SD is produced through forward T b simulations with the HUT model using the interpolated effective grain size and land cover information. The simulations are compared via a cost function at each grid cell with spaceborne radiometer measurements. The assimilation adaptively weighs the spaceborne brightness temperature observations and the observed SD field from the first step to estimate of the final SD. An estimate of statistical uncertainty (in the form of a variance) is also derived on a grid-cell basis. The system level implementation of the SWE service at FMI consists of a variety of in-house programs and scripts, intermediate and long-term file storage directories as well as FTP-servers. The associated programs and scripts handling data conversions and transfers are all controlled by a single master script producing the Icelandic SWE maps and transferring the data to a dedicated FTP-server at FMI. The data is fetched by the IMO from the SNAPS FTP-server and published on the SNAPS website for all interested end-users. There are six primary steps in the implementation of the SWE service: FMI Preprocessing: Snow depth observations from the Icelandic weather stations are spatially interpolated onto a snow depth grid. The grids are transferred via FTP-protocol to FMI s snowflake.fmi.fi server with an in-house built program written in C. FTP Cronjob / Conversion to EASE Grid: The latest SSMI/S images are retrieved from MeteoAM s FTP-server and converted from Swath-geometry to EASE grids by an in-house built program written in C. Conversion to CSV: A MatLab script is used to convert the snow depth grids to.csv formatted matrices, which in-turn are used in the snow depth assimilation scheme. Calculating dry snow mask and snow melt mask: Areas of wet snow, i.e. areas of melting snow, are masked according to observed brightness temperature values using an empirical equation based on (takala et al. 2009). Dry snow areas are determined through a cumulative process whereby the masked area is increased incrementally for each time step from the beginning of the snow season. In-contrast, during the spring season, the snow melt mask is used to clip the dry snow masked areas. These masking procedures are handled with a MatLab script. Page 12

17 Figure 3.4. System level flowchart of the SWE service implementation. Compilation / Post-Processing: A series of MatLab scripts are used to compile the final SWE product. The three components of the final SWE product; brightness temperature derived SWE estimate grids, interpolated continuous snow depth.csv matrices and the dry/wet snow masks are combined to form SWE estimate grids and SWE variance grids for Iceland. Packaging and FTP Upload: Raw binary MatLab format SWE data are converted to GeoTIFFformat. The final GeoTIFF-files are uploaded to the SNAPS data storage system with an in-house built Bash script Current status and on-going issues Passive microwave retrievals can discriminate snow water amount in areas of small snow cover up to 200 mm of SWE or so. For heavier snow conditions the product detects heavy snow but cannot be expected to discern exact quantities. The passive microwave retrievals require substantial coupling to synoptic snow station measurements. The effective resolution and sensitivity of this product within a region is therefore largely controlled by the availability of stations in that region. In areas of sparse synoptic snow measurements, fluctuations in results can be very large while on larger scales and over longer periods the product should show good correlation to the mean snow condition. The Iceland target area posed a particular challenge due to sub-pixel orography (mountains) and sparse snow station measurements, especially in the highlands. Current SWE values for Iceland are Page 13

18 therefore noisier than for other target regions. That being said, they do show a tendency towards placing the main weight of the snow in the correct areas. The IMO will be conducting experiments with extracting weekly or monthly averages for comparison with other snow maps, measurements and model data. By incorporating an SWE map production for Iceland we have opened the possibility that suitable use for these maps will be found. Furthermore, the winter of is also the first time these retrievals are processed for Iceland, so we may yet discover issues that can be improved in the SWE production. The IMO conducted snow thickness and snow water equivalent (SWE) measurements in the Vestfirðir target area during the spring of This data as well as other in-situ measurements conducted around Iceland will be used for validation purposes. Page 14

19 4. Reflectance snow cover maps for Iceland (IMO) At the onset of the SNAPS project, the IMO had no operational processes for remote sensing of snow. One goal of WP4 was the transfer of snow-mapping expertise between the participating institutions. A good first step for the IMO was to adopt reflectance snow mapping into the MODIS image processing chains already operational at the IMO. MODIS is an advanced multispectral (visible, IR and thermal IR) camera on board the NASA earth observation satellites Terra and Aqua. Timeliness, data accessibility and data quality made MODIS an attractive choice for this first snow mapping chain at the IMO. During the development, FMI provided valuable guidance and assistance based on their own expertise in reflectance snow-mapping 4.1. Methodology Reflectance snow mapping utilizes a strong inversion in snow-reflectance between the visible and near infra-red spectrum of light. At visible wavelengths there is a strong backwards scattering of light while at near-infrared wavelengths snow displays a high forward scattering (absorption). This high contrast difference in reflectance means that images composed of both visible and near-infrared wavelengths effectively discriminate snow and ice from common objects in the image scene, such as clouds, waters and soil. A commonly applied approach in deriving snow cover fraction from satellite reflectance channels is to utilize an effective snow cover indicator called the Normalized Difference Snow Index (NDSI). This index can then be calibrated between a low, snow-free NDSI value (0% snow cover) and a high NDSI value (100% snow cover) to derive the inferred snow cover fraction. This methodology is based directly on NASA s own MODIS snow cover algorithm as described by Hall et. al (2002) and the fractional snow cover calibration scheme demonstrated by Salomonson and Appel (2004). However, certain extensions were implemented by the IMO to correct for great variability in snow free NDSI values observed across the land surface type in Iceland. The cloud masking method implemented for the snow cover product a simple, yet effective, algorithm developed by the Finnish Environmental Administration (SYKE) for global snow mapping within the ESA's GlobSnow project ( System development history Main development phase of IMO's snow mapping production chain began in October 2011 and continued until the end of August IMO s own production chain for MODIS processing was utilized, with the new snow mapping chain developed as an additional component to that system. Page 15

20 Figure 4.1. The Terra - NDSI snow-free calibration map integrated from July 2012 data. The snow-free NDSI values vary greatly depending on the surface type. Sandy areas have a notably high snow-free value. Figure 4.2. A map detailing the number of snow free samples acquired in the month of July The map is complete, however some areas of Iceland were cloudy for extensive periods. The following list gives an outline of steps taken in developing the production chain: 1. Implemented sun-angle correction to reflectance channel in MODIS. This was necessary for maintaining equivalent cloud detection thresholding for difference illuminations by the sun. 2. Implemented the GlobSnow Simple Cloud Detection Algorithm (SCDA) and applied slight tuning for MODIS and Iceland. In addition, developed corrective steps to handle erroneous detection at glacial sand to snow/ice fringe ( an Iceland specific issue ). 3. Implemented the cloud-masked Normalized Difference Snow Index (NDSI) byproduct. 4. Implemented a production-chain for ground resolution maps calculated from satellite zenith angle data sets. 5. Implemented a mosaicking algorithm for seaming together a number of cloud masked maps into a single daily mosaic. The algorithm takes account of image resolution, prioritizing the overlay of higher ground resolution samples. 6. Collected snow-free NDSI samples of Iceland in the month of July 2013 and generated a snow-free NDSI calibration map for better fractional snow-cover scaling from a lower-bound snow-free NDSI (0%) to a fixed high-bound NDSI (100%). 7. The snow cover product was at least partially verified by comparison with snow cover observed through road-side web cameras. The web camera located on Steingrímsfjarðarheiði showed very good comparison with both the Aqua and Terra products during the April June melting period in NRT fractional snow cover production was fully initiated in September Handling of erroneous snow detection in cloud and mountain shadows added in October Page 16

21 Figure 4.3. An RGB composite image, visually highlighting snow-covered areas (red) from 5 October Compare with snow cover product on the right. Figure 4.4. A fractional snow-cover map produced on 5 October Compare with Raw RGB composite image on left The NDSI calibration The NDSI measures the relative reflectance in two channels, one visible channel and another nearinfrared channel. A different channel combination had to be used for the Aqua satellite, due to the sub-nominal quality of its 1.6 micrometer channel (MODIS channel 6). The 2.1 micrometer channel (MODIS channel 7) was substituted instead: As a result, two separate snow calibration maps and production chains were implemented for MODIS on Aqua and Terra. The dynamic range of NDSI values with the 2.1 micrometer channel is somewhat smaller than those with the 1.6 micrometer channel. The Aqua snow cover product is therefore more sensitive to bias and noise. In contrast to NASA s own global MODIS snow cover product, the product developed by the IMO have the advantage of being adapted to Iceland in terms of cloud masking but also in terms of snow index calibration, which is very dependent on soil type in the case of Iceland. The extensive glacial sands in the highlands of Iceland posed a particular problem, showing an unusually high snow free NDSI value, much higher than reported as globally representative values published in the literature. The use of NDSI calibration maps therefore represent an important innovation in reflectance snow mapping for Iceland Current status and on-going issues As of February 2013 the snow mapping chain has been functioning well, although a few new minor issues were discovered along the way, such as erroneous snow cover detection in mountain shadows. Additional shadow detection thresholds were added to improve these issues. Also the Page 17

22 cloud masking is currently somewhat excessive near and around mountainous areas. Further attempts are therefore being made to further tune the cloud masking codes. The snow mapping shows severe degradation in quality beginning near the end of November until beginning of February. These errors result due to the low solar angle in the period around winter solstices. This is a fully expected weakness of reflectance snow mapping and is in line with other similar reflectance products. Currently the IMO is considering either to indicate the low solar angle error in some manner or to completely mask out the production for this period. The latter is more common. Page 18

23 5. Data sharing and presentation In the process of implementing work package 4 it became clear that various data needed to be shared efficiently among the cooperating institutions to facilitate the development of the remote sensing retrievals. Furthermore there was an understanding among the cooperating institutes that all SNAPS products and product images would be stored in a common location so that they could more easily be researched and incorporated into the SNAPS website in near real time. For presenting the snow map time series, it was desirable to provide the users with an easy way of flicking through the map series both in time but also by type Data archive During the WP4 development stage the sharing of auxiliary data, such as regional Synoptic measurements was needed. FMI, with its extensive resources in data archiving, offered to set up and host the SNAPS ftp archive for data and product sharing among the cooperating institutes. The archive has also functioned well for sharing all remote sensing data products and images. The image products that are now being displayed, in near real time, on the SNAPS website ( are either automatically acquired or produced from data injected into this archive. In the future, this particular data portal can in principle be opened to the general public, but this remains to be decided. The use of this portal or some other means of public data sharing depends on security and cost issues Image player The IMO took on the work to add an interface to the SNAPS snow map time series as it coincided well with IMO s work on interfacing with its own historical weather map archive. It was decided that an image player/scroller was practical to implement aiming at allowing the user to easily shift through maps and compare different map products with similar time stamps. The player was implemented as a HTML page with JavaScript client-side code for scanning (AJAX) a web directory at IMO for images and buffering them in memory for scrolling. The player is currently hosted on a web server at IMO but is embedded into the SNAPS website. Separate instances of this player were set up for each target region including only those maps relevant to that region. The players for different target regions can be accessed at the following URL: or by selecting the snow-maps menu item on the front page. Page 19

24 Figure 5.1. The image player. Users can use the arrow buttons to step backwards and forwards in time or alternatively by scrolling the mouse wheel. The i-button at top right of the player window provides information on the product being viewed. Currently all SNAPS remote sensing products have been embedded into the player and the plan is to continue adding new maps as higher level products become available from other work packages. Effort was made to have the player supported by most recent web browsers. It was tested on and should work on recent versions of Firefox, Internet Explorer, Chrome and Opera. The player does fail on a number older web browser versions and it can in theory fail on future versions of web browsers. These compatibility problems are mainly due to inconsistencies in interpretation of the JavaScript (ECMAScript) standard by web browser developers, but sometimes also due to bugs. In the near future, the IMO plans to improve the player s browser compatibility by incorporating the jquery library for abstracting browser dependencies as much as possible. Currently the player s image caching can be heavy and unresponsive over very slow internet connections. The IMO plans to implement thumbnailing of the image archive to improve this aspect of the system. Page 20

25 6. Conclusions WP 4 progress has been good, and all tasks have been completed. Two issues (the loss of Envisat ASAR and AMSR-E sensors) had an impact on the service at an intermediate time scale. The issues were solved by replacing input data with data from alternative sensors (Radarsat-2 and SSM/I). This solved many of the problems caused by loss of sensors, but since the sensor capabilities are somewhat different with respect to coverage and spatial resolution, we also observe that the current real-time service is not as good as it could have been with the original sensors. It should also be noticed that MODIS is an old sensor and, although there are two instruments on Terra and Aqua, there is a need to prepare for sensor loss here as well. The real-time service has progressed quite satisfactory during the winter Many snow maps have been produced relatively shortly after acquisition for all the target areas. In periods, there has been little data available for one of the target areas (Iceland). This was caused by the acquisition plan of Radarsat-2, which we have no control over, except for Norwegian territories Recommendations for further work WP 4 has now ended. Some of the work related to snow processing chains will be continued in WP5. The snow maps produced in this work package should be evaluated and to some extent validated with auxiliary data such as models, in situ data and high resolution optical snow maps (if available). As Radarsat-2 is a commercial sensor with potential high costs, there is a need to look for alternative data sources. ESA Sentinel-1 will be launched in the fall of 2013 and will, after the commissioning phase, provide real-time data from the winter This will probably be too late to grant valuable datasets for the last year of SNAPS, but upcoming projects/continuation of service will need to consider this sensor. Both MODIS carrying satellites, Terra and Aqua, have far exceeding their expected lifetimes. Therefore all processing system based on MODIS should soon be adapted to data from newer instruments such as VIIRS and/or Sentinel-3 when these data sources become operationally available. Page 21

26 References Hall, D.K., Riggs, G.S., Salomonson, V.V., DiGiromamo, N. & Bayr, K.J. (2002). MODIS Snow-Cover Products. Remote Sensing of Environment, 83, Nagler, T. & Rott, H. ( Retrieval of wet snow by means of multitemporal SAR data. IEEE Transactions on Geoscience and Remote Sensing. 38, Pulliainen, J., Grandell, J. & Hallikainen, M. T. (1999). HUT Snow Emission Model and its Applicability to Snow Water Equivalent Retrieval. IEEE Transactions on Geoscience and Remote Sensing. 37, Pulliainen, J. (2006). Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations. Remote Sensing of Environment. 101: Salomonson, V.V. & Appel, I. (2004). Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment, 89, Storvold, R., Malnes, E. & Lauknes, I. (2005). Using ENVISAT ASAR Wideswath data to retrieve snow covered area in mountainous regions. EARSeL eproceedings. 4, Takala, M., Pulliainen, J., Metsämäki, S. & Koskinen, J. (2009). Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to IEEE Transactions on Geoscience and Remote Sensing. 47 (9): Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P, Koskinen, J. & Bojkov, B. (2011). Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, Vol. 115, Issue 12, 15 December 2011, , DOI: /j.rse Page 22