Snow depth retrieval in North-Western Himalayan region using pursuit-monostatic TanDEM-X datasets applying polarimetric synthetic aperture radar interferometry based inversion Modelling

摘要

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m x 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.