# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
###### Abstract  
Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 \(\times\) 100 km\({}^{2}\). The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters' bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better.  
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is a coherent side-looking radar system used to monitor the Earth with high-resolution images thanks to its capacity to electronically simulate a huge antenna. In coherent imaging systems, a speckle is a signal-dependent noise from a coherent summation of backscattered signals from multiple distributed targets.  
More specifically, speckle noise looks like grainy salt and pepper in radar images, and the presence of these patterns reduces the clarity of the images. Despite speckle effects, the SAR measurements of Sentinel-1 have relevant information content, particularly of interest for monitoring cold regions. Studies by [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26], among many others, exploited these observations to better understand the state and evolution of wet snow, including its spatial variability and how it changes over time.  
Methods for detecting wet snow using SAR data usually rely on a change detection approach with an image ratio that uses two images with and without snow, assuming that backscatters of wet snow are generally smaller than those of dry snow or snow-free areas. The ratio is calculated for the same study area, and a threshold is then applied to derive a snow mask [27]. One of the reference studies on the subject is by [16], who used ratios of Sentinel-1 images with a threshold of \(-2\) dB combining VV and VH polarizations. Using a 2-3 dB threshold is not relevant everywhere and may introduce detection errors in some situations, depending on, for example, its surface type, or its roughness (see, for example, [28]). The SAR signal variation can be limited in forest areas, sometimes being less than the 2-3 dB range.  
The main objective of this study was to explore various families of image segmentation methods for snow monitoring in mountain areas. The segmentation techniques were carefully selected, based on different criteria, and tested on the Sentinel-1 SAR image time series over a season characterised by wet snow. The aim was to find ways to improve wet snow detection methods through simple techniques that are easy to implement and require low computational costs. The tested methods apply to other topics, making them versatile and practical in various applications. Additionally, we aimed to evaluate the segmentation results by selecting independent optical images as targets and computing several evaluation scores. This provides a comprehensive assessment of the effectiveness of the segmentation methods.