A new method for large scale snow depth estimates using Sentinel-1 and ICESat-2

Abstract. Knowledge about seasonal snow accumulation is important for managing water resources, but accurate estimates of snow depth at a high spatiotemporal resolution are sparse, especially in mountainous regions. In this paper, we outline a novel approach to estimate snow depths using Sentinel-1 C-band synthetic aperture radar (SAR) and ICESat-2 LiDAR observations. Specifically, we estimate snow depths at 500-meter spatial resolution by correlating increase in Sentinel-1 volume scattering with snow depths derived using ICESat-2. Sentinel-1’s vast spatial coverage and frequent 6/12-day revisit cycle makes it promising for monitoring seasonal snow accumulation, but capturing the volume scattering signal within a dry snowpack and relating it to snow depth remains challenging. Using ICESat-2, we retrieve thousands of high accuracy snow depth observations covering the Southern Norwegian Mountains. ICESat-2 has a low revisit time of three months, but by matching observations with the temporally nearest Sentinel-1 scene, we significantly enhance spatiotemporal resolution. Our results demonstrate that our ICESat-2 calibrated Sentinel-1 snow depths can estimate snowfall magnitudes in deep dry snow (>0.6 meters), achieving an accuracy of 0.5–0.7 meters, significantly improving estimates made by the SeNorge snow model in remote mountainous regions. This study highlights the potential of utilizing ICESat-2 to derive Sentinel-1 snow depths, improving snow monitoring capabilities in data-sparse regions.