Dynamical System Approach for Wet Snow Retrieval in Mountains Using Sentinel-1 SAR Images

We present a novel iterative method for segmenting Sentinel-1 SAR images to estimate the extent of wet snow in the mountains. The algorithm consists of a discrete-time dynamical system fed by various variables, including radar amplitude images and terrain information (elevations, slopes, orientations), and controlled by different parameters. The dynamical system uses the SAR amplitude ratio (ratio between an SAR and a reference image without snow) as an initial condition and makes it evolve iteratively toward a segmented image. A digital terrain model modulates the connection between pixels in the discrete-time dynamical system, thereby ensuring a physical consistency in the iterative algorithm. This algorithm is tested over the 2017-2018 season and its outputs are compared with the Copernicus state-of-the-art snow/wet snow products and with expert estimates of snow elevations at the scale of the Grandes-Rousses alpine French massif. We show that the dynamical system can be used to derive wet snow maps in very good agreement with independent data. Furthermore, the elevations and dates of snow retreat estimated by the dynamical system are found to be in much better agreement with estimates obtained from optical satellites and forecaster expertise when compared with the SAR-based Copernicus products for southern facing slopes.