Change Detection and Image Time-Series Analysis 1

5Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series

Fatima KARBOU¹, Guillaume JAMES^{2, 3}, Philippe DURAND³ and Abdourrahmane M. ATTO⁴

¹Université Grenoble Alpes, University of Toulouse, CNRM, CNRS, Centre d’Études de la Neige at Météo-France, Grenoble, France

²Inria Grenoble – Rhône-Alpes Research Center, France

³CNES, Toulouse, France

⁴University Savoie Mont Blanc, Annecy, France

5.1. Introduction

In mountain regions, seasonal snow monitoring is very important for many applications such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. For example, a good knowledge of snow extent, of its evolution over time and in particular the starting date of snow-melt is of prime importance for hydro power production and for anticipating flood risks. A good knowledge of starting date of snow-melt is also of great importance for mountain ecosystem studies. Space-based remote sensing allows monitoring of seasonal snow at scales of time and space that are not comparable to ground-based measuring stations. The Sentinel-1 satellites provide cloud insensitive C-band Synthetic Aperture Radar (SAR) data and allow snow cover to be observed day and night at unprecedented temporal and spatial resolutions. The sensitivity of the measurements to snow liquid water content has enabled the development and evaluation of algorithms for detecting wet snow (Baghdadi et al. 1998; Magagi and Bernier 2003; Nagler et al. 2016; Karbou et al ...