Towards a Pan-European snow cover and melt extent product from Sentinel-1 SAR and Sentinel-3 SLSTR Data

Abstract

<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>

Monitoring snow wetness evolution from satellite with Sentinel-1 multi-track composites

“…Across all tests, the liquid water content was among the strongest correlation values (see appendixes A3, A4 and A5). This coincides with the ongoing understanding of the measured signal loss being related to the amount of liquid water within a snowpack (e.g., Lund et al, 2022;Nagler et al, 2021). With increasing water presence, radar attenuation increases and measured signal decreases.…”

“…Adaptive thresholds based on the underlying land cover class (Liu et al, 2022) have recently been suggested. However, we have used the established and widely applied threshold of 2 dB for Sentinel-1 data (Nagler et al, 2021(Nagler et al, , 2016, results indicated in Fig. 2).…”

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

“…As moisture increases, the SAR backscattering reduces in intensity since part of it is absorbed by the water particles [43,80,81]. The followed approach foresees the use of two images (i) a reference image acquired during snow-free or dry conditions [19,34,47,80,82]; and (ii) an image of the period of interest, such as the melting season. The difference of the two images allows the detection of main changes such as those related to wet snow presence and reduces the influence of other factors such as the topography.…”

“…Active sensors are commonly used in local scale application. In particular, the new Sentinel-1 (S-1) SAR constellation, which has a dual polarization capability, very short revisit times, high spatial resolution, rapid product delivery, has open access for all imagery [46], and has become the most common source used in different applications that exploit wet snow information [19,[47][48][49].…”