{"citation":{"mla":"Shaw, Thomas E., et al. “Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing.” <i>Water Resources Research</i>, vol. 56, no. 2, e2019WR024880, American Geophysical Union, 2020, doi:<a href=\"https://doi.org/10.1029/2019wr024880\">10.1029/2019wr024880</a>.","ama":"Shaw TE, Gascoin S, Mendoza PA, Pellicciotti F, McPhee J. Snow depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic remote sensing. <i>Water Resources Research</i>. 2020;56(2). doi:<a href=\"https://doi.org/10.1029/2019wr024880\">10.1029/2019wr024880</a>","short":"T.E. Shaw, S. Gascoin, P.A. Mendoza, F. Pellicciotti, J. McPhee, Water Resources Research 56 (2020).","ieee":"T. E. Shaw, S. Gascoin, P. A. Mendoza, F. Pellicciotti, and J. McPhee, “Snow depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic remote sensing,” <i>Water Resources Research</i>, vol. 56, no. 2. American Geophysical Union, 2020.","ista":"Shaw TE, Gascoin S, Mendoza PA, Pellicciotti F, McPhee J. 2020. Snow depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic remote sensing. Water Resources Research. 56(2), e2019WR024880.","chicago":"Shaw, Thomas E., Simon Gascoin, Pablo A. Mendoza, Francesca Pellicciotti, and James McPhee. “Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing.” <i>Water Resources Research</i>. American Geophysical Union, 2020. <a href=\"https://doi.org/10.1029/2019wr024880\">https://doi.org/10.1029/2019wr024880</a>.","apa":"Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., &#38; McPhee, J. (2020). Snow depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic remote sensing. <i>Water Resources Research</i>. American Geophysical Union. <a href=\"https://doi.org/10.1029/2019wr024880\">https://doi.org/10.1029/2019wr024880</a>"},"month":"02","date_created":"2023-02-20T08:12:47Z","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"status":"public","publication_identifier":{"issn":["0043-1397"],"eissn":["1944-7973"]},"date_updated":"2023-02-28T12:26:14Z","volume":56,"extern":"1","type":"journal_article","publication_status":"published","publication":"Water Resources Research","article_number":"e2019WR024880","abstract":[{"text":"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.","lang":"eng"}],"_id":"12598","author":[{"first_name":"Thomas E.","full_name":"Shaw, Thomas E.","last_name":"Shaw"},{"last_name":"Gascoin","full_name":"Gascoin, Simon","first_name":"Simon"},{"full_name":"Mendoza, Pablo A.","first_name":"Pablo A.","last_name":"Mendoza"},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti","full_name":"Pellicciotti, Francesca","first_name":"Francesca"},{"last_name":"McPhee","full_name":"McPhee, James","first_name":"James"}],"doi":"10.1029/2019wr024880","keyword":["Water Science and Technology"],"date_published":"2020-02-01T00:00:00Z","year":"2020","article_type":"original","publisher":"American Geophysical Union","day":"01","intvolume":"        56","language":[{"iso":"eng"}],"issue":"2","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1029/2019WR024880"}],"quality_controlled":"1","scopus_import":"1","title":"Snow depth patterns in a high mountain Andean catchment from satellite optical tristereoscopic remote sensing"}