Potential of X-band polarimetric SAR co-polar phase difference for Arctic snow depth estimation

• Main Authors: Voglimacci-Stephanopoli, Joëlle, Wendleder, Anna, Lantuit, Hugues, Langlois, Alexandre, Stettner, Samuel, Dedieu, Jean-Pierre, Roth, Achim, Royer, Alain
• Other Authors: Université de Sherbrooke (UdeS)
• Format: Report
• Language: English
• Published: HAL CCSD 2021
• Subjects: geo; envir; Arctic
• Online Access: https://doi.org/10.5194/tc-2021-314; https://hal.archives-ouvertes.fr/hal-03402739

Abstract. Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar—SAR) can address the issue and outperform methods based on passive microwaves. Thus, high spatial resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) quantified the spatio-temporal variability of the geophysical properties of the snowpack in an Arctic catchment, we then (2) studied the links between snow properties and CPD, considering ground vegetation. Snow depth (SD) could be extracted using the CPD when certain conditions are met. A high incidence angle (> 30°) with a high Topographic Wetness Index (TWI) (> 7.0) showed correlation between SD and CPD (R-squared up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.