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Poster De Conférence Année : 2022

Pan-European snow cover products from Sentinel-2: algorithm and evaluation

Résumé

The snow cover area, defined as the spatial extent of the snow cover on the land surface, is a key variable in many hydrology, climatology and ecology studies. Earth observation satellites have been used to routinely map the snow cover area at continental scale since the late 1960s. However, most available products have a spatial resolution of 500 m and above and therefore do not meet a range of user needs for both science and operational applications. On behalf of the European Commission, the European Environment Agency has commissioned the development and real-time production of the Copernicus High Resolution Snow & Ice products (HRSI), including a snow cover component to address these needs. In particular, this service provides a canopy-adjusted fractional snow cover (FSC) at 20 m resolution along with a cloud and cloud shadow mask and quality flags. The products are derived from Sentinel-2 observations, resulting in a revisit time less than or equal to 5 days (except during low illumination periods in winter at high latitude). The products are distributed with a maximal latency of 3 h after the availability of the level 1C product in the Sentinel-2 mission ground segment, which means that they are generally available on the same day as the sensing time. The products are generated using the MAJA-LIS pipeline. MAJA is a level 2A processor which provides slope corrected surface reflectance images including a coarse resolution cloud and cloud shadow mask, while the Let-It-Snow (LIS) algorithm provides the fractional snow cover (FSC) of every pixel identified as containing snow and a refined cloud mask. To detect snow and refine the cloud mask from MAJA, the LIS algorithm relies on a digital elevation model and four threshold parameters for the differentiation between no-snow, snow, and cloud pixels. A sigmoid-shaped empirical function for estimating the FSC from the Normalized Difference Snow Index of the level 2A pixels was calibrated using Pléiades very high resolution images. In forest regions, the FSC is adjusted to subcanopy “on-ground” FSC using the Copernicus Sentinel-2-derived tree cover density. The snow cover detection was evaluated over Europe using in situ snow depth observations at 1764 stations from 36 countries, covering a wider range of climate and topographic conditions. We found a good agreement between both datasets with an accuracy (proportion of correct classifications) of 94 % and kappa of 0.81. More accurate (+6 % kappa) retrievals were obtained by excluding low-quality pixels at the cost of a reduced coverage (−13 % data). Performances decreased at sites with higher tree cover density due to the obstruction of the ground surface by the tree canopy. The FSC in open terrain was evaluated using SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements yielding a root mean square error (RMSE) of about 25%. A study over an instrumented forested site in Sierra Nevada (USA) showed that the operational HRSI algorithm yielded similar performances (25-30% RMSE) as a computationally intensive spectral unmixing approach while retrieving the subcanopy ground FSC.
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Dates et versions

hal-03686189 , version 1 (02-06-2022)

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Zacharie Barrou Dumont. Pan-European snow cover products from Sentinel-2: algorithm and evaluation. Living Planet Symposium, May 2022, Bonn, Germany. ⟨10.5194/tc-15-1343-⟩. ⟨hal-03686189⟩
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