Snow Depth Retrieval from L-Band Data Based on Repeat Pass InSAR Techniques
The goal of this study is to understand the pattern of snow distribution over mountain ranges and the capability of L-band Synthetic Aperture Radar (SAR) data to retrieve snow depth. Ground-based snow records and Airborne Lidar and SAR data collected as part of NASA's snow expedition over Mores Creek Summit in 2021 were employed for this study. The preliminary result shows that co-polarization particularly VV has better coherence and thus most optimal for snow monitoring. The impact of large temporal baseline, vegetation and elevation on coherence were analyzed. Result shows that decorrelation increases with vegetation and temporal separation as expected but decreases with elevation. A good agreement exists between lidar snow depth and snow depth recorded by SNOTEL. Snow depth retrieved from the UAVSAR data captured snow accumulation and melt pattern between the satellite acquisition dates as confirmed by the snow depth record at SNOTEL study site. Atmospheric correction of the phase change is required to improve the accuracy of InSAR techniques for snow depth estimation. This study will contribute to existing efforts in the snow science community to understand the capability of future satellite missions such as NISAR, a U.S-Indian satellite that is planned to operate on L-band.
Idowu, Adebisi Naheem and Marshall, Hans-Peter. (2022). "Snow Depth Retrieval from L-Band Data Based on Repeat Pass InSAR Techniques". In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 4248-4251). IEEE. https://doi.org/10.1109/IGARSS46834.2022.9884723