| Title: |
Inferring Spatiotemporal Snow Variation in the French Alps from Sentinel-1. |
| Authors: |
Turbé, Clémence; Karbou, Fatima; Rabatel, Antoine; Monteiro, Diego; Mauss, Adrien; Fructus, Mathieu |
| Source: |
Journal of Applied Meteorology & Climatology; Nov2025, Vol. 64 Issue 11, p1767-1784, 18p |
| Subject Terms: |
SNOW cover; SPATIO-temporal variation; REMOTE sensing; HYDROLOGY; METEOROLOGICAL precipitation; REMOTE-sensing images; CLIMATOLOGY |
| Geographic Terms: |
ALPS |
| Abstract: |
In mountainous regions, a precise estimation of the snow-cover area is crucial for a wide range of applications (hydrology, climatology, etc.). We present a new approach for estimating the total snow-cover area in the French Alps using Sentinel-1 data, which are basically only used to detect the presence of wet snow. Wet snow monitoring is of interest for many applications, including hydrology and risk assessment. Our method involves calculating the likelihood of wet snow using Sentinel-1 images and a digital elevation model to obtain an estimate of the probability of wet snow for elevation and orientation classes. By identifying the snow lines (the elevation at which the snow is present homogeneously) and the transition from wet snow to dry snow, we can infer the classes associated with dry snow and thus get an estimation of the total snow cover. Time series of wet and total snow cover are reconstructed over a decade (2015–24). Estimates of total snow cover are compared with the snow-cover area from MODIS and Sentinel-2. We study the interannual, seasonal, and spatial variability of wet and total snow over two massifs in the French Alps. Crocus snow model is used to infer the elevation of snow lines from snow depth simulations and to infer wet snow presence from the liquid water content, which are compared to the snow cover derived from Sentinel-1. We show that monitoring (total/dry/wet) snow-cover elevation at the massif scale provides useful information to study snow-cover variability. Significance Statement: We demonstrate the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images, supplemented with topography information, to estimate wet and total snow coverage. These advancements are a great way to complement the use of optical images, which are inherently lacking in data due to cloudy conditions. This work seeks to analyze and assess the spatial and interannual variability of SAR-based reconstructed snow cover in regions of the French Alps, using additional remote sensing data, such as optical imagery from MODIS and Sentinel-2. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Journal of Applied Meteorology & Climatology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |