Fine-scale, sub-annual satellite stereo observations of snow cover and snow depth can help improve quantification of snow water equivalent at critical times during the accumulation and ablation season. We are refining very-high-resolution (VHR) spaceborne optical stereo methods to generate spatially-continuous digital surface models (DSMs) and maps of snow depth and snow water equivalent (SWE) over mountain sites in the Western U.S. 

In this work, we leverage the open-source software of NASA’s Ames Stereo Pipeline for extensive and iterative testing of stereogrammetric processing parameters to produce snow-free and snow-covered DSMs. Using open-source tools, we customize and improve automated surface co-registration using snow-free DSMs generated from spaceborne stereogrammetry and airborne lidar. High-resolution land cover classification maps derived from the input stereo images using machine learning methods improve the co-registration results and snow depth product quality. 

We assess our stereo-derived DSM and snow depth mapping methods across multiple sites in Colorado using USGS 3D Elevation Program (3DEP) and the Airborne Snow Observatory (ASO) airborne lidar DSMs and snow depth products. We present initial evaluations of our surface elevation reconstructions across variable terrain and land cover. Finally, we use a bulk density approach and empirical density models to convert snow depth maps into maps of snow water equivalent. 

We are developing a user-friendly notebook for the full workflow with default processing parameters tuned for mountain terrain. We hope that these tools will enable new users with limited photogrammetry experience to produce maps of snow depth and snow water equivalent from VHR satellite imagery.