Abstract
Abstract. Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.
Highlights
Snow cover characterization and estimation of snow geophysical parameters are significant for understanding the hydrological budget of the glacial rivers and for studying the snow surface melt processes
We present a linear physical model based on a feature space derived from the observations of near infrared (NIR) reflectance and normalized differenced snow index (NDSI)
The proposed method is invariant to atmospheric factors since it utilizes the NIR reflectance instead of other variables such as the land surface temperature which is significantly affected by atmospheric composition
Summary
Snow cover characterization and estimation of snow geophysical parameters are significant for understanding the hydrological budget of the glacial rivers and for studying the snow surface melt processes. Alpine snow and glaciers constitute a significant part of the cryosphere which yields through melt runoff one of the major usable water resources. For countries like India which rely significantly on glacier and snowmelt runoffs for water resource, the understanding of snowmelt processes is significant. Forecasting of snowmelt runoff requires timely information on the spatial-temporal distribution of the geophysical parameters of snow such as the liquid water content (LWC), density of snow, and the depth of the snowpack (Shi and Dozier, 1995). The continuous monitoring of snowpack variables is significant in avalanche forecasting. In the Himalayas, due to higher elevation and difficult terrains, often several areas remain inaccessible, especially during the winter season, imposing several constraints on the possibility of field campaigns for in-situ measurements of snow geophysical parameters
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
