Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data

• Main Authors: Deschamps-Berger, César, Gascoin, Simon, Shean, David, Besso, Hannah, Guiot, Ambroise, López-Moreno, Juan I.
• Other Authors: Centre National D'Etudes Spatiales (France), Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), National Aeronautics and Space Administration (US), Ministerio de Ciencia, Innovación y Universidades (España)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: Hannah; The Cryosphere
• Online Access: http://hdl.handle.net/10261/344545; https://doi.org/10.5194/tc-17-2779-2023; https://doi.org/10.13039/501100002830; https://doi.org/10.13039/100000104; https://doi.org/10.13039/501100011033; https://doi.org/10.13039/501100004837

The unprecedented precision of satellite laser altimetry data from the NASA ICESat-2 mission and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in mountains, a critical variable for ecosystem and water resource monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for 3 years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off digital elevation models. Snow depth derived from ATL06 data only (snow-on and snow-off) offers a poor temporal and spatial coverage, limiting its potential utility. However, using a digital terrain model from airborne lidar surveys as the snow-off elevation source yielded a snow depth accuracy of ∼ 0.2 m (bias) and precision of ∼ 1 m (random error) across the basin, with an improved precision of 0.5 m for low slopes (< 10∘), compared to eight reference airborne lidar snow depth maps. Snow depths derived from ICESat-2 ATL06 and a satellite photogrammetry digital elevation model have a larger bias and reduced precision, partly induced by increased errors in forested areas. These various combinations of repeated ICESat-2 snow surface elevation measurements with satellite or airborne products will enable tailored approaches to map snow depth and estimate water resource availability in mountainous areas with limited snow depth observations. This work has been supported by the Programme National de Télédétection Spatiale (PNTS; grant no. PNTS-2018-4), by the Centre National d’Études Spatiales (CNES), and by the Spanish Ministry of Science and Innovation (MARGISNOW project, grant no. PID2021-124220OB-100; HIDROIBERNIEVE project, grant no. CGL2017-82216K). Ambroise Guiot was supported by Météo-France during the internship which laid the groundwork for this article. David Shean was supported by NASA (award no. 80NSSC20K0995). Hannah Besso was supported by NASA (award no. 80NSSC20K1293). Peer reviewed