<p>Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.</p><p>In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.</p><p>Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.</p><p>The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.</p><p>In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.</p>
Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.
<p>Seasonal snow is one of the terrestrial essential climate variables specified by the Global Climate Observing System (GCOS). With a coverage of about 45 to 50 Mio. km&#178; of the global land area during the main winter season in the past decades, seasonal snow is the largest component of the cryosphere having a major impact on different processes of the Earth&#8217;s system.</p><p>Different medium resolution optical satellite data have been exploited in the past few years to monitor the seasonal snow extent on local to global scale. Most of these satellite-based products provide information on the snow viewable from space, i.e. in forested areas the snow viewable on top of the forest canopy, and many of these products provide only binary classification on snow, i.e. a pixel is either snow covered or snow free.</p><p>In the frame of the ESA Climate Change Initiative Extension (CCI+) Snow, a new climate data record (CDR) of daily global snow cover fraction maps with about 1 km pixel spacing was generated from Terra MODIS and Sentinel-3 SLSTR data for the period 2000 &#8211; 2020. The daily products of this CDR provide the fraction of snow covered area per pixel in percentage not only for all land areas, but differentiate in forested areas two thematic snow information, the snow cover fraction viewable from above, and the snow cover fraction on the ground. The retrieval method assures that the classified snow cover fraction on ground and the viewable snow cover fraction information are consistent for all observed land areas, allowing the usage of the data sets in different applications. Each daily product contains the unbiased root mean square error per observed pixel as uncertainty estimation. The CDR will be publicly released via the ESA Open Data Portal soon.</p><p>Based on the new CDR, the variability of the seasonal snow in the past 20 years is analysed, investigating in detail interannual, seasonal and monthly trends on global and hemispheric scales. The maximum global snow cover in the past 20 years shows overall a negative trend, although the derived interannual variations reach up to 5 Mio. km&#178;. The analysis of the seasonal snow extent indicates no significant trend of the maximum snow cover during the main winter season on the Northern Hemisphere (January &#8211; March) in the past 20 years. But during the onset and the melting seasons of the Northern Hemisphere, all the trends of the maximum snow area are negative, with the most negative signal in May.</p><p>We will present the method used for the generation of the new snow cover fraction CDR from MODIS and SLSTR data and discuss the results of the spatial and temporal analyses of the 20-years time series of global daily snow cover fraction products, including also analyses of the variations in the timing and duration of the snow season for selected regions in the context of the changing climate.</p>
<p>The synergistic use of data from different satellites of the Sentinel series offers excellent capabilities for generating high quality products on key parameters of the global climate system and environment. A main parameter for climate monitoring, hydrology and water management is the seasonal snow cover. In the frame of the ESA project SEOM S1-4-SCI Snow, led by ENVEO, we developed, implemented and tested a novel approach for mapping the total extent and melting areas of the seasonal snow cover by synergistically exploiting Sentinel-1 SAR and Sentinel-3 SLSTR data and apply these tools for snow monitoring over the Pan-European domain.</p><p>Whereas data of medium resolution optical sensors are used for mapping the total snow extent, data of the Copernicus Sentinel-1 mission in Interferometric Wide Swath (IW) mode at co- and cross-polarizations are used for mapping the extent of snowmelt areas applying change detection algorithms. In order to select an optimum procedure for retrieval of snowmelt area, we conducted round-robin experiments for various algorithms over different snow environments, including high mountain areas in the Alps and in Scandinavia, as well as lowland areas in Central Europe covered by grassland, agricultural plots, and forests. In mountain areas the tests show good agreement between snow extent products during the melting period derived from SAR data and from Sentinel-2 and Landsat-8 data. In lowlands ambiguities may arise from temporal changes in backscatter related to soil moisture and agricultural activities. Dense forest cover is a major obstacle for snow detection by SAR because the surface is masked by the canopy layer which is a major scattering source at C-band. Therefore, areas with dense forest cover are masked out. Based on this results we selected for the retrieval of snowmelt area a change-detection algorithm using dual-polarized backscatter data of S1 IW acquisitions. The algorithm applies multi-channel speckle filtering and data fusion procedures for exploiting VV- and VH-polarized multi-temporal ratio images. The binary SAR snowmelt extent product at 100 m grid size is combined with the Sentinel-3 SLSTR and MODIS snow products in order to obtain combined maps of total snow area and melting snow. The optical satellite images provide information on snow extent irrespective of melting state but are impaired by cloud cover. For generating a fractional snow extent product from MODIS and Sentinel-3 SLSTR data we apply multi-spectral algorithms for cloud screening, the discrimination of snow free and snow covered regions and the retrieval of fractional snow extent. In order to fill gaps in the optical snow extent time sequence due to cloud cover we apply a data assimilation procedure using a snow pack model driven by numerical meteorological data of ECMWF, simulating daily changes in the snow extent. We present the results of the Pan-European snow cover and melt extent product derived from optical and SAR data. The performance of this product is evaluated in different environments using independent validation data sets including in-situ snow and meteorological measurements, snow products from Sentinel-2 and Landsat images, as well as high resolution numerical meteorological data.</p>
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