@article{fdi:010084618, title = {{P}rocessing of {VEN} mu {S} images of high mountains : a case study for cryospheric and hydro-climatic applications in the {E}verest region ({N}epal)}, author = {{B}essin, {Z}. and {D}edieu, {J}. {P}. and {A}rnaud, {Y}ves and {W}agnon, {P}atrick and {B}run, {F}. and {E}steves, {M}ichel and {P}erry, {B}. and {M}atthews, {T}.}, editor = {}, language = {{ENG}}, abstract = {{I}n the {C}entral {H}imalayas, glaciers and snowmelt play an important hydrological role, as they ensure the availability of surface water outside the monsoon period. {T}o compensate for the lack of field measurements in glaciology and hydrology, high temporal and spatial resolution optical remotely sensed data are necessary. {T}he {F}rench-{I}sraeli {VEN} mu {S} {E}arth observation mission has been able to complement field measurements since 2017. {T}he aim of this paper is to evaluate the performance of different reflectance products over the {E}verest region for constraining the energy balance of glaciers and for cloud and snow cover mapping applied to hydrology. {F}irstly, the results indicate that a complete radiometric correction of slope effects such as the {G}amma one (direct and diffuse illumination) provides better temporal and statistical metrics ({R}-2 = 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}). {S}econdly, a mixed spectral-textural approach on the {VEN} mu {S} images strongly improves the cloud mapping by 15% compared with a spectral mask thresholding process. {T}hese findings will improve the accuracy of snow cover mapping over the watershed areas downstream of the {E}verest region.}, keywords = {optical remote sensing ; {VEN} mu {S} ; glaciers ; snow ; mountains ; cloud ; mapping ; {NEPAL} ; {EVEREST}}, booktitle = {}, journal = {{R}emote {S}ensing}, volume = {14}, numero = {5}, pages = {1098 [29 ]}, year = {2022}, DOI = {10.3390/rs14051098}, URL = {https://www.documentation.ird.fr/hor/fdi:010084618}, }