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

Atto, Abdourrahmane M.; James, Guillaume; Durand, P.

doi:10.1002/9781119882268.ch5

Change Detection and Image Time Series Analysis 1 2021

DOI: 10.1002/9781119882268.ch5

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

Abdourrahmane M. Atto

Guillaume James

P. Durand

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des labor… Show more

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“…This article does not address the impact of reference image selection on wet snow detection. The study in [42] showed that examining the cross-correlations between images calculated over an entire time series allows for a better selection of reference images (with less similarity to the winter period). This approach was tested in [38] to better detect avalanche debris.…”

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Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

et al. 2023

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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 × 100 km2. 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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Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

et al. 2023

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Identification of Seasonal Snow Phase Changes from C-band SAR Time Series with Dynamic Thresholds

Beltramone

Frery

Rotela

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

et al. 2021

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The main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate the extent of wet snow cover continuously over a season and an analysis of the influence of complex mountain topography on snow distribution is proposed taking into account altitude, slope, and aspect of the terrain. The Sentinel-1 wet snow extent product was compared with Sentinel-2 snow products for cloud free scenes. We show that while there are good agreements between the two satellite products, differences exist, especially in areas of forests and glaciers where snow is underestimated. This underestimation must be considered alongside the areas of geometric distortion that were excluded from our study. We analysed retrievals at the scale of our study area by examining wet snow Altitude–Orientation diagrams for different classes of slopes and also wet snow Altitude–Time diagrams for different classes of orientations. We have shown that this type of representation is very useful to get an overview of the snow distribution as it allows to identify very easily wet snow lines for different orientations. For an orientation of interest, the Altitude–Time diagrams can be used to track the evolution of snow to locate altitudes and dates of snow loss. We also show that ascending/descending Sentinel-1 image time series are complementary to monitor wet snow over the French alpine areas to highlight wet snow altitude ranges and identify melt-out days. Links have also been made between Sentinel-1 responses (wet snow) and snow/meteorological events carefully listed over the entire 2017–2018 season.

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

Cited by 3 publications

References 16 publications

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Identification of Seasonal Snow Phase Changes from C-band SAR Time Series with Dynamic Thresholds

Monitoring Wet Snow Over an Alpine Region Using Sentinel-1 Observations

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