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.

Satellite remote sensing often requires a compromise between spatial resolution and spatial coverage for timely and accurate measurements of earth-system processes. But in recent years, increased availability of submeter-scale imagery dramatically altered this balance. Commercial satellite imagery from DigitalGlobe and Planet offer on-demand, very high-resolution panchromatic stereo and multispectral (MS) image collection over snow-covered landscapes, with individual image coverage of up to ~1900 km2. Repeat stereo-derived digital elevation models can be used to accurately estimate snow depth. Integration of contemporaneous ~1–2 m land cover classification maps can provide precise snow-covered area (SCA) products and improved processing, analysis, and interpretation of these snow depth estimates. We are developing machine learning classification algorithms to identify snow, vegetation, water, and exposed rock using varying combinations of available bands (panchromatic, 4/8-band multispectral, SWIR) and band ratios (e.g. NDVI, NDSI) from these products. We present findings for NASA SnowEx campaign sites (Grand Mesa and Senator Beck Basin, CO) and other snow monitoring sites in the Western U.S. using WorldView-3, PlanetScope, and Landsat 8 imagery. Preliminary results show that a tuned random forest algorithm using WorldView-3 MS and SWIR bands yielded the most accurate estimates of SCA of all band combinations and imagery products. With the power to resolve individual trees, these products offer direct measurements of SCA, without the need to account for mixed pixels and fractional SCA as with lower-resolution products. This open-source workflow will be used to process longer time-series and larger areas in a semi-automated fashion, allowing for rapid analysis, increased portability, and broader utility for the community.