Advancements in Snow Cover Monitoring Based on Synergy of Sentinel-1 SAR and Sentinel-3 SLSTR Data

13 Nov 2018, 10:50
20m
Ice and Snow Ice & Snow Session

Speaker

Dr Thomas Nagler (ENVEO IT GmbH)

Description

The synergistic use of data from different satellites of the Sentinel series offers excellent capabilities for generating advanced products on parameters of the global climate system and environment. A key 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 applying this on the Pan-European domain.
Data of the Copernicus Sentinel-1 mission, operating over land surfaces 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. Therefore, areas with dense forest cover are masked out. Based on the results of the round-robin tests, we selected for the retrieval of snowmelt area a change-detection algorithm using dual-polarized backscatter data of S1 IW acquisitions over land. 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 snow product in order to obtain combined maps of total snow area and melting snow. Complementary to the melt snow extent from SAR, 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 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 area extent. In order to fill gaps in the snow extent time sequence due to cloud cover, we apply a data assimilation procedure using a snow pack model driven by numerical meteorological data from ECMWF, simulating daily changes in the snow extent. We present the results of round robin experiments on SAR wet snow algorithms and their performance in different environments, and show time series of synergistic snow products using SAR and optical satellite data over the Pan-European domain.

Primary authors

Dr Thomas Nagler (ENVEO IT GmbH) Prof. Helmut Rott (ENVEO IT) Ms Joanna Ossowska (ENVEO IT GmbH) Dr Gabriele Schwaizer (ENVEO IT GmbH) Dr David Small (University of Zurich) Dr Eirik Malnes (NORUT) Dr Kari Luojus (Finnish Meteorological Institute) Dr Sari Metsaemaeki (Finnish Environment Institute) Dr Simon Pinnock (ESA Climate Office)

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