A novel snow depth estimation model for the Eastern Himalayas using DInSAR

Assessing snow depth is crucial for various environmental, hydrological, and climatological studies. Snow depth measurement utilizing conventional techniques often encounters challenges in high-altitude remote locations. In this scenario, space-borne remote sensing has proven to be a scientific tool to estimate snow depth. With the availability of Sentinel-1, a C-band Synthetic Aperture Radar (SAR) data, snow depth estimation is being widely studied. This study introduces an improved snow depth inversion model utilizing the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique in conjunction with inputs from Landsat-9. The Level 2, Landsat-9 dataset is used to estimate the snow wetness and the snow cover map. The corresponding state-of-the-art empirical equations are utilized to predict snow dielectric, an important input for the snow depth inversion model. The performance of the proposed snow depth inversion model shows improvement after implementing the modified weight function and scaling values to the existing model. The final snow depth inversion model achieves a Root Mean Squared Error (RMSE) of 5.74 cm and a Mean Absolute Error (MAE) of 4.94 cm, demonstrating significant accuracy in estimating snow depth over the alpine regions of the Eastern Himalayas. The intermediate accuracy values (9.58 cm and 7.90 cm) are mentioned in the study for context. This may contribute to a better understanding of snowpack dynamics and enhance the efficacy of water resource management strategies in alpine areas of the Eastern Himalayas.