Mapping snow depth in open alpine terrain from stereo satellite imagery

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70aEuro-m resolution images were acquired by the Pl,iades satellite over an open alpine catchment (14.5aEuro-km(2)) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4aEuro-m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48aEuro-m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pl,iades-derived snow depths. The median of the residuals is -0.16aEuro-m, with a standard deviation (SD) of 0.58aEuro-m at a pixel size of 2aEuro-m. We compared the 2aEuro-m Pl,iades dDEM to a 2aEuro-m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1aEuro-km(2)). The UAV-derived snow depth map exhibits the same patterns as the Pl,iades-derived snow map, with a median of -0.11aEuro-m and a SD of 0.62aEuro-m when compared to the snow-probe measurements. The Pl,iades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.