Mountain snow depth retrievals from customized processing of ICESat-2 satellite laser altimetry

Abstract

Snow depth is highly variable across basins, yet most snow depth data in the western U.S. come from sparse in situ point measurements. The water resources community needs accurate snow depth data for improved basin-wide snow depth estimates. The NASA ICESat-2 mission has provided over four years of global satellite laser altimetry measurements since October 2018. Previous studies have shown that standard ICESat-2 data products, when combined with snow-off digital terrain models (DTMs) from airborne laser scanning, have the potential to provide snow depth measurements with varying accuracy depending on factors such as surface slope and canopy cover. In this study we show that ICESat-2 snow depth measurements can be improved with customized data products generated using the SlideRule Earth service. Here we investigate the accuracy of our ICESat-2 SlideRule snow depth method using four years (2019–2022) of reference in situ snow depth measurements and airborne lidar snow depth observations for two watersheds with varying terrain characteristics: the Tuolumne River basin above Hetch Hetchy, CA and the Methow Valley, WA. We observe median differences of −0.14 m (RMSE of 0.18 m) and −0.20 m (RMSE of 0.33 m) between our ICESat-2 snow depth measurements and reference snow depth measurements for the Tuolumne Basin and Methow Valley sites, respectively. While individual ICESat-2 elevation measurements can contain noise, basin-scale aggregation offers robust statistics for snow depth. Differences in accuracy between sites are attributed to terrain characteristics and their spatial distributions. The customized ICESat-2 SlideRule data products used in this study resulted in more accurate median snow depth values, including under canopy, than those found by previous studies using standard ICESat-2 data products in mid-latitude mountainous regions. When combined with snow-off DTMs, the aggregated snow-on ICESat-2 SlideRule observations could provide a new snow depth dataset across the western U.S. and potentially global land surfaces.