资源环境科技发展态势分析平台

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 Pleiades 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 Pleiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40-50 degrees (2%). We correct and filter the Pleiades 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 similar to 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 Pleiades, even when forced with locally calibrated data. Comparing these alternative model approaches to corrected Pleiades snow depths reveals total snow volume differences between -28% (DBSM) and +54% (TOPO) for the catchment and large differences across most elevation bands. Pleiades 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.