This is a revised preprint submitted to the Elsevier CRST journal and hosted by EarthArXiv. Snow Depth and Snow Water Equivalent Estimation in 1 the Northwestern Himalayan Watershed using 2 Spaceborne Polarimetric SAR Interferometry 3 Sayantan Majumdar a,b,∗ , Praveen K. Thakur b , Ling Chang a , 4 Shashi Kumar b , Sneh Mani c 5 a Faculty of Geo-information Science and Earth Observation (ITC), University of Twente 6 b Indian Institute of Remote Sensing (IIRS), ISRO 7 c Snow and Avalanche Study Establishment (SASE), DRDO 8 Abstract 9 Snow depth (SD) and Snow Water Equivalent (SWE) constitute essential physical properties of snow 10 and find extensive usage in the hydrological modelling domain. However, the prominent impact of 11 the hydrometeorological conditions and difficult terrain conditions inhibit accurate measurement 12 of the SD and SWE— an ongoing research problem in the cryosphere paradigm. In this context, 13 spaceborne synthetic aperture radar (SAR) systems benefit from global coverage at sufficiently high 14 spatial and temporal resolutions. Still, existing polarimetric and interferometric SAR techniques 15 are susceptible to high volume scattering resulting from the increased snow grain sizes due to 16 the standing (or old) snow formation driven by the temperature induced snow metamorphosis 17 process. Hence, to model this volume decorrelation, the polarimetric SAR interferometry (Pol- 18 InSAR) technique can be effectively applied. In this work, the standing snow depth (SSD) and 19 its corresponding standing snow water equivalent (SSWE) are estimated using the single-baseline 20 Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude 21 inversion model. To achieve this, six TerraSAR-X, TanDEM-X Coregistered Single look Slant 22 range Complex (CoSSC) bistatic quad-pol acquisitions between December 2015 and January 2016 23 over Dhundi (situated in the Beas watershed, northwestern Himalayas, India) are used. Due 24 to the associated problems of model parameter tuning, complex topographical conditions, and 25 limited ground-truth measurements, appropriate sensitivity analyses have been carried out for 26 the parameter optimisation. Furthermore, the uncertainty sources are identified by performing a 27 summer (June 8, 2017) and wintertime (January 8, 2016) comparative analysis of the study area 28 which quantitatively highlights the changes in the percentages of the surface and volume scatterings. 29 Evidently, the improved model displays sufficiently high overall SSD accuracy with coefficient of 30 determination (R 2 ) ≈ 0.96, Mean Absolute Error (MAE) ≈ 1.61 cm, and Root Mean Square Error 31 (RMSE) ≈ 2.16 cm. Additionally, the respective SSWEs have been calculated by assuming a fixed 32 snow density for each epoch wherein the overall error metrics are R 2 ≈ 0.71, MAE ≈ 5.19 mm, 33 and RMSE ≈ 6.84 mm. Therefore, this research successfully demonstrates the practicability of the 34 improved Pol-InSAR model for SD estimation over rugged terrains. 35 Keywords: Pol-InSAR, Microwave Remote Sensing, Synthetic Aperture Radar, Polarimetry, 36 Interferometry, Snow Depth, Snow Water Equivalent, Watershed, Sensitivity Analysis 37 * Corresponding author Email address: ir.sayantan.majumdar@gmail.com ( Sayantan Majumdar ) Majumdar et al., 2020 Cold Regions Science and Technology 1