Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithmsin the western part of the Tianshan Mountains Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithmsin the western part of the Tianshan Mountains

最小化 最大化

Vol17 No.4: 884-897

Title】Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithmsin the western part of the Tianshan Mountains

Author】LIUYang1,2,3,4; CHEN Xi3; HAO Jian-Sheng1,2,3,4; LI Lan-hai1,2,3,4*

Addresses】1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2 Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences, Xinyuan 835800, China; 3 Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China; 4 Xinjiang Regional Center of Resources and Environmental Science Instrument, Chinese Academy of Sciences, Urumqi 830011, China

Corresponding author】LI Lan-hai

Citation】Liu Y, Chen X, Hao JS, et al. (2020) Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains. Journal of Mountain Science 17(4). https://doi.org/10.1007/s11629-019-5723-1

DOI】https://doi.org/10.1007/s11629-019-5723-1

Abstract】Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine (PCA–SVM) method is proposed for snow cover mapping through the integration of moderate-resolution imaging spectroradiometer (MODIS) snow cover products and the Sentinel-1 synthetic aperture radar (SAR) scattering characteristics. First, derived from the Sentinel-1A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis (PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation (FB1=93.86, FB2=59.78). The evaluation of the threat score (TS), probability of detection (POD), and false alarm ratio (FAR) for the snow-covered pixels obtained from the two-stage SAR images were different (TS1=86.84, POD1=90.10, FAR1=4.01; TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.

Keywords】Snow cover; Estimation; Sentinel-1/2; MODIS; Machine learning