Abstract:The Sentinel satellite constellation series, developed by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in a wide range of application domains. Here, we address the potential of the Sentinel-1 mission for mapping and monitoring the surface velocity of glaciers and ice sheets. We present an ice velocity map of Greenland, derived from synthetic aperture radar (SAR) data acquired in winter 2015 by Sentinel-1A, the first satellite of the Copernicus program in orbit. The map is assembled from about 900 SAR scenes acquired in Interferometric Wide swath (IW) mode, applying the offset tracking technique. We discuss special features of IW mode data, describe the procedures for producing ice velocity maps, and assess the uncertainty of the ice motion product. We compare the Sentinel-1 ice motion product with velocity maps derived from high resolution SAR data of the TerraSAR-X mission and from PALSAR data. Beyond supporting operational services, the Sentinel-1 mission offers enhanced capabilities for comprehensive and long-term observation of key climate variables, such as the motion of ice masses.

Abstract:The Sentinel satellite constellation series, developed and operated by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in environment, climate and security. We developed, tested and evaluated an algorithm for generating maps of snowmelt area from C-band synthetic aperture radar (SAR) data of the Sentinel-1 mission. For snowmelt classification, a change detection method is applied, using multitemporal dual-polarized SAR data acquired in Interferometric Wide swath (IW) mode, the basic operation mode over land surfaces. Of particular benefit for wet snow retrievals are the high instrument stability, the high spatial resolution across the 250 km wide swath, and the short revisit time. In order to study the impact of polarization, we generated maps of melting snow using data of the VV-polarized channel, the VH-polarized channel and a combined VV-and VH-based channel using a weighting function that accounts for effects of the local incidence angle. Comparisons are performed with snow maps derived from Landsat images over study areas in the Alps and in Iceland. The pixel-by-pixel comparisons show good agreement between the snow products of the two sensors, with the best performance for retrievals based on the combined (VV and VH) channel and a minor decline for the VH-based product. The VV-based snowmelt extent product shows a drop-off in quality over areas with steep terrain because of the decreasing backscatter contrast of snow-covered versus snow-free surfaces on fore-slopes. The investigations demonstrate the excellent capability of the Sentinel-1 mission for operational monitoring of snowmelt areas.

Abstract. We use repeat-pass SAR data to produce detailed maps of surface motion covering the glaciers draining into the former Larsen B Ice Shelf, Antarctic Peninsula, for different epochs between 1995 and 2013. We combine the velocity maps with estimates of ice thickness to analyze fluctuations of ice discharge. The collapse of the central and northern sections of the ice shelf in 2002 led to a near-immediate acceleration of tributary glaciers as well as of the remnant ice shelf in Scar Inlet. Velocities of most of the glaciers discharging directly into the ocean remain to date well above the velocities of the pre-collapse period. The response of individual glaciers differs and velocities show significant temporal fluctuations, implying major variations in ice discharge as well. Due to reduced velocity and ice thickness the ice discharge of Crane Glacier decreased from 5.02 Gt a −1 in 2007 to 1.72 Gt a −1 in 2013, whereas Hektoria and Green glaciers continue to show large temporal fluctuations in response to successive stages of frontal retreat. The velocity on Scar Inlet ice shelf increased 2-3-fold since 1995, with the largest increase in the first years after the break-up of the main section of Larsen B. Flask and Leppard glaciers, the largest tributaries to Scar Inlet ice shelf, accelerated. In 2013 their discharge was 38 % and 46 % higher than in 1995.

Abstract. We use repeat-pass SAR data to produce detailed maps of surface motion covering the glaciers draining into the former Larsen B ice shelf, Antarctic Peninsula, for different epochs between 1995 and 2013. We combine the velocity maps with estimates of ice thickness to analyze fluctuations of ice discharge. The collapse of the central and northern sections of the ice shelf in 2002 led to a near-immediate acceleration of tributary glaciers as well as of the remnant ice shelf in Scar Inlet. Velocities of the glaciers discharging directly into the ocean remain to date well above the velocities of the pre-collapse period. The response of individual glaciers differs and velocities show significant temporal fluctuations, implying major variations in ice discharge and mass balance as well. Due to reduced velocity and ice thickness the ice discharge of Crane Glacier decreased from 5.02 Gt a−1 in 2007 to 1.72 Gt a−1 in 2013, whereas Hektoria and Green glaciers continue to show large temporal fluctuations in response to successive stages of frontal retreat. The velocity on Scar Inlet ice shelf increased two- to three fold since 1995, with the largest increase in the first years after the break-up of the main section of Larsen B. Flask and Leppard glaciers, the largest tributaries to Scar Inlet ice shelf, accelerated. In 2013 their discharge was 38%, respectively 45%, higher than in 1995.

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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. km² 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’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 – 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. km². 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 – 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>

<div> <p><span>Copernicus Land Monitoring Service has recently launched a group of high-resolution snow cover products which are derived from Sentinel-1 and Sentinel-2 constellations. High- Resolution Snow and Ice Monitoring (HRSI) products include Fractional Snow Cover (FSC) from the Sentinel-2 constellation and Wet and Dry Snow (WDS) covering Europe and SAR Wet Snow (SWS) products for selected mountain regions derived from the Sentinel-1 constellation. The FSC and WDS products have gaps in the snow cover data due to cloud presence and the SWS product provides only information on the melting snow extent, but dry snow areas and snow-free areas cannot be discriminated by means of SAR data. In the same portfolio, we provide the daily cumulative Gap-filled Fractional Snow Cover (GFSC) product, which is a fusion of those three products. In this product, we gap-fill the FSC product using the wet snow presence detected by the SWS in the spatial domain. In the temporal domain, all recent data in the last 7 days are used for gap-filling by temporal composition. The product aims to have a complete snow cover map of Europe.</span><span> </span></p> </div><div> <p><span>The quality of the product is assessed using in-situ data and </span><em><span>gap simulation, </span></em><span>for the period of 09.2017 - 08.2018 for mountain ranges in the Pyrenees, Alps, Scandinavia , East Turkey and Corsica, covered by 34 Sentinel-2 tiles. In-situ snow depth information is converted to binary snow cover information using a snow depth threshold. For the gap simulation method, as first step, FSC products with observed snow information are selected. Then, an artificial cloud mask is overlaid on these products, and the gap-filling method is run to generate GFSC products. The resulting GFSC products are compared with the corresponding observed FSC products, considering them as reference data. This comparison shows the agreement between the FSC product and the gap-filling methods. For both comparison methods, FSC values in GFSC and FSC products are converted to binary snow cover information using an FSC threshold. Resulting binary snow cover information is used in contingency tables and performance metrics are calculated for the product and for different gap-filling methods.</span><span> </span></p> </div><div> <p><span>We have found that the gap-filling provides 5 times more pixels with snow cover information and the quality is fairly good. The comparison with in-situ data shows an accuracy over 88% in temporal gap-filling and precision over 87% in spatial gap-filling. The comparison of the gap simulated GFSC and the FSC products shows an accuracy over 97% in temporal gap-filling and precision over 83% in spatial gap-filling. Temporal gap-filling performance is consistent throughout the seasons, although it is less accurate in the accumulation season for the spatial gap-filling, which is expected as the wet snow algorithm is developed for the melting season conditions. The assessment shows that the methods are working well and 7 days old FSC, WDS and SWS data are still valid to fill the gaps in the data.</span><span> </span></p> </div>