Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

最小化 最大化

Vol16 No.6: 1435-1451

Title】Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

Author】Divyesh VARADE1*; Onkar DIKSHIT1; SurendarMANICKAM2

Addresses】1 Geoinformatics, Department of Civil Engineering, Indian Institute of Technology Kanpur, Kalyanpur, Kanpur-208016, India; 2 Department of Civil and Environmental Engineering, Duke University, Durham, NC-27708-028, USA

Corresponding author】Divyesh VARADE

Citation】Varade D, Dikshit O, Manickam S (2019) Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data. Journal of Mountain Science 16(6). https://doi.org/10.1007s11629-019-5373-3

DOI】https://doi.org/10.1007s11629-019-5373-3

Abstract】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.

Keywords】Snow mapping; Ratio method; Normalized Differenced Snow Index; Classification; Polarimetric synthetic-aperture radar