Spaceborne polarimetric sar interferometry for snow depth retrieval in the northwestern Himalayan watershed

Sayantan Majumdar, Praveen K. Thakur, Ling Chang, Shashi Kumar

Research output: Contribution to conferenceAbstractAcademic


Snow depth (SD) is an important physical property of snow which is predominantly utilized in hydrologic studies. However, due to the terrain difficulties and hydrometeorological conditions, accurate large-scale SD estimation is still an ongoing research element in the cryosphere domain.
Spaceborne synthetic aperture radar (SAR) systems have been extensively used to measure different snow physical properties. Still, previously developed polarimetric SAR (PolSAR) and interferometric SAR (InSAR) techniques are affected by volume decorrelation in rugged surfaces. Therefore, the polarimetric SAR interferometry (Pol-InSAR) technique can be effectively applied to obtain the volumetric SD. In this work, the standing (or old) snow depth (SSD) is computed using the single-baseline Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude inversion model.
Here, the study area (~96 km2) surrounding Dhundi (Beas watershed, India) is selected from the TerraSAR-X, TanDEM-X coregistered bistatic scene acquired on January 8, 2016. Since the Pol-InSAR model involves several user-defined parameters, necessary sensitivity analyses are performed for fine-tuning purposes.
Furthermore, the summer (June 8, 2017) and wintertime (January 8, 2016) images are used to quantitatively compare the different scattering mechanisms. This is performed using the dual-pol H-훼 decomposition and unsupervised Wishart classification techniques. The results indicate that snow clearly increases the high entropy anisotropic volume scattering (by 5.11%) and reduces the medium entropy volume scattering (by 7.01%). Similarly, the other scatterings are also affected in the presence of snow.
Additionally, the reference ALOS PALSAR DEM is used to analyze the elevation and local incidence angle errors using the field measurements. Noticeably, these errors do not significantly alter the SSD values because of the ensemble averaging operations. Also, the Pol-InSAR model is improved by using the secant root-finding algorithm, leading to more precise and accurate SSD estimates.
Evidently, the mean SSD obtained over the 3✕3 (81 m2) window at Dhundi is consistent with the in-situ measurement. This showcases the practicability of the improved Pol-InSAR model for estimating the SSD over undulating terrains.
Original languageEnglish
Number of pages1
Publication statusPublished - 22 Sept 2019
EventGSA Annual Meeting 2019 - Phoenix, United States
Duration: 22 Sept 201925 Sept 2019


ConferenceGSA Annual Meeting 2019
Abbreviated titleGSA 2019
Country/TerritoryUnited States
Internet address


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