Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data
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We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar (SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index (NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03% by volume.
KeywordsSnow mapping Ratio method Normalized Differenced Snow Index Classification Polarimetric synthetic-aperture radar
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This work is partly supported by Project number DST-2016056, funded by the Department of Science and Technology, Government of India. The authors acknowledge the MacDonald, Dettwiler and Associates Ltd. (2018) and the Canadian Space Agency (CSA) for the RADARSAT-2 product. The raw test datasets can be downloaded freely for non-commercial research purposes through the Copernicus open access hub https://doi.org/scihub.copernicus.eu/. The processed test datasets are available on request.
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