Processing of VENµS Images of High Mountains: A Case Study for Cryospheric and Hydro-Climatic Applications in the Everest Region (Nepal)

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 5; p. 1098
• Main Authors: Bessin, Zoé, Dedieu, Jean-Pierre, Arnaud, Yves, Wagnon, Patrick, Brun, Fanny, Esteves, Michel, Perry, Baker, Matthews, Tom
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022; MDPI
• Subjects: Albedo; cloud mapping; Clouds; Digital Elevation Models; Energy balance; Environmental Sciences; Glaciers; Glaciology; Global Changes; Hydrologic cycle; Hydrology; Mapping; Methods; Mountains; optical remote sensing; Radiometric correction; Regions; Remote observing; Remote sensing; Sensors; Snow; Snow cover; Snowmelt; Spatial discrimination; Spatial resolution; Stream flow; Surface water; Topography; VENµS; Watersheds; Himalaya Mountains; Nepal; Mount Everest; glaciers; mountains; VENμS; snow; cloud mapping
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14051098

In the Central Himalayas, glaciers and snowmelt play an important hydrological role, as they ensure the availability of surface water outside the monsoon period. To compensate for the lack of field measurements in glaciology and hydrology, high temporal and spatial resolution optical remotely sensed data are necessary. The French–Israeli VENµS Earth observation mission has been able to complement field measurements since 2017. The aim of this paper is to evaluate the performance of different reflectance products over the Everest region for constraining the energy balance of glaciers and for cloud and snow cover mapping applied to hydrology. Firstly, the results indicate that a complete radiometric correction of slope effects such as the Gamma one (direct and diffuse illumination) provides better temporal and statistical metrics (R2 = 0.73 and RMSE = 0.11) versus ground albedo datasets than a single cosine correction, even processed under a fine-resolution digital elevation model (DEM). Secondly, a mixed spectral-textural approach on the VENµS images strongly improves the cloud mapping by 15% compared with a spectral mask thresholding process. These findings will improve the accuracy of snow cover mapping over the watershed areas downstream of the Everest region.