Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing

Abstract

Obtaining detailed information about high mountain snowpacks is often limited by insufficient ground‐based observations and uncertainty in the (re)distribution of solid precipitation. We utilize high‐resolution optical images from Pléiades satellites to generate a snow depth map, at a spatial resolution of 4 m, for a high mountain catchment of central Chile. Results are negatively biased (median difference of −0.22 m) when compared against observations from a terrestrial Light Detection And Ranging scan, though replicate general snow depth variability well. Additionally, the Pléiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40–50° (2%). We correct and filter the Pléiades snow depths using surface classification techniques of snow‐free areas and a random forest model for data gap filling. Snow depths (with an estimated error of ~0.36 m) average 1.66 m and relate well to topographical parameters such as elevation and northness in a similar way to previous studies. However, estimations of snow depth based upon topography (TOPO) or physically based modeling (DBSM) cannot resolve localized processes (i.e., avalanching or wind scouring) that are detected by Pléiades, even when forced with locally calibrated data. Comparing these alternative model approaches to corrected Pléiades snow depths reveals total snow volume differences between −28% (DBSM) and +54% (TOPO) for the catchment and large differences across most elevation bands. Pléiades represents an important contribution to understanding snow accumulation at sparsely monitored catchments, though ideally requires a careful systematic validation procedure to identify catchment‐scale biases and errors in the snow depth derivation.

WITHDRAWN: Optical Satellite Sensor and Positioning Accuracy Assessment for The Hongqi-H9 Wide-Range Satellite in Different Terrains

“…Compared with that in ground space, the compensation in image space is more rigorous theoretically [31], as indicated in equation (2).…”

“…Despite the rapid advancement in satellite remote sensing technology, two key requirements to obtain high spatial resolution satellite images remain to be attained [1][2][3]. The first requirement corresponds to the fixed-point fast revisit capability, which is primarily influenced by the timeliness of the satellites [4][5]; for instance, satellites can generally provide real-time images of disaster areas within a few minutes in natural disaster events [6].…”

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

“…Half of the points sampled in the field were on slopes lower than 10 • , while the median terrain slope in this catchment is ∼ 30 • . This lack of validation data in steep slope areas was an important limitation of this study since DEMs from stereoscopic images are known to be less accurate on steep slopes due to a higher sensitivity to horizontal error and to local image distortion (Lacroix, 2016;Shean et al, 2016). In addition, snow probe measurements may fail to represent the mean HS at a scale of a 2 m pixel, especially in mountain terrain (Fassnacht et al, 2018).…”

“…The method was first tested using two Pléiades stereo triplets over the Bassiès catchment in the Pyrenees (Marti et al, 2016). The snow-on and snow-off DEMs were generated using the Ames Stereo Pipeline (ASP; Shean et al, 2016;Beyer et al, 2018) and coregistered before differencing (Berthier et al, 2007). The accuracy of the method was evaluated using 451 probe measurements of snow depth.…”

Digital Elevation Model Quality Assessment Methods: A Critical Review

“…• whatever the accuracy of the two DEMs, any horizontal registration error Δx between them will Another example of DEM-derived topographic object is the variable layer of sand, snow, or ice over the ground surface. Many studies consist in measuring and analyzing the thickness of these layers based on the difference between two DEMs produced at different dates [166,167]. The quality of the result depends on the cumulative quality of both DEMs.…”