Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow &amp; Ice Monitoring Service

Dumont, Zacharie Barrou; Gascoin, Simon; Hagolle, Olivier; Ablain, M.; Jugier, Rémi; Salgues, Germain; Marti, Florence; Dupuis, Andrea; Dumont, Marie; Morin, Samuel

doi:10.5194/tc-15-4975-2021

Cited by 16 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on optical data, these products are inherently limited by the following factors (e.g. Barrou Dumont et al, 2021;Keuris et al, 2023):…”

Section: Snow Cover Fractionmentioning

confidence: 99%

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Cluzet,

Magnusson,

Quéno

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Distributed energy and mass-balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet snow maps could potentially help refining the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction pertains predominantly to the ablation season. In this work, wet snow maps, derived from Sentinel-1 and snow cover fraction (SCF) retrieval from Sentinel-2 are compared against model results from a fully distributed energy-balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet snow line (WSL) to compare Sentinel-1 wet snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals insufficient snow melt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed achieving earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow line (SL) observations were also substantially reduced. These results suggest that wet snow maps contain valuable real-time information for snowpack models, nicely complementing flat-field snow depth observations, particularly in complex terrain and at higher elevations. The persisting correlation between wet snow line and snow line biases provides insights into refined development, tuning and data assimilation methodologies for operational snow-hydrological modelling.

show abstract

“…Based on optical data, these products are inherently limited by the following factors (e.g. Barrou Dumont et al, 2021;Keuris et al, 2023):…”

Section: Snow Cover Fractionmentioning

confidence: 99%

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Cluzet,

Magnusson,

Quéno

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The performance of the snow detection using the MAJA-LIS pipeline was assessed on the French Alps and Pyrenees with an accuracy of 94% (kappa 0.83) [18]. A more comprehensive evaluation was conducted at pan European scale and yielded comparable results [29]. However, where transparent or semi-transparent clouds are present, LIS conservatively keeps them as cloud pixels, although they could be visually identified as snow [30].…”

Section: B Sentinel-2mentioning

confidence: 99%

Snow and Cloud Classification in Historical SPOT Images: An Image Emulation Approach for Training a Deep Learning Model Without Reference Data

Barrou Dumont,

Gascoin,

Inglada

2024

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

View full text Add to dashboard Cite

The lack of revisit in long-term satellite time series such as Landsat is an issue to assess ecosystems response to snow cover variations in mountains. A recent release of the SPOT 1-5 satellite images collection by the SPOT World Heritage (SWH) program offers the opportunity to increase the temporal revisit of Landsat from 1986 to 2015 at 20 m resolution. However, spectral and radiometric limitations of these images hinder the application of well-established pixel-wise methods to extract the snow cover area. As a work-around, deep learning techniques such as convolutional neural networks can incorporate both spectral and spatial information to classify every pixel as snow, cloud, or snow-free. However, the lack of reference data poses a challenge to the implementation of such data-driven approaches. Here, we develop an emulator of SPOT images which takes as input Sentinel-2 images. As a result, an emulated SPOT image can be paired with a reference snow map generated from its source Sentinel-2 image to train a deep learning model able to process actual SPOT images. We follow this approach to train a U-Net and evaluate different training strategies. We apply the different models to classify actual SPOT images for which we have reference data for validation. The method yields high precision in detecting snow, with minimal false snow pixel identification. This is at the cost of overestimating cloud pixels around clouds and highly saturated areas. The results confirm the potential of this method to generate time series of snow cover maps using the SWH collection.

show abstract

“…We expect that the improved radiometric resolution and addition of new bands (such as the red edge) will help improve the predictions of snow in forest understories in the near future. Moreover, in general, snow under a forest canopy is challenging to observe via optical methods because of forest canopy cover and the resulting low signal-to-noise ratio [57,58]. Additional availability of bands (e.g., shortwave infrared (SWIR)) would also enable use of the Normalized Difference Snow Index (NDSI) that might better the predictability of the model in forests [23,59] and further gives the ability to mimic MODIS-type explorations that utilize broader band availability [44,48].…”

Section: Limitationsmentioning

confidence: 99%

High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery

et al. 2022

View full text Add to dashboard Cite

Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins.

show abstract

Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service

Cited by 16 publications

References 17 publications

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Using Sentinel-1 wet snow maps to inform fully-distributed physically-based snowpack models

Snow and Cloud Classification in Historical SPOT Images: An Image Emulation Approach for Training a Deep Learning Model Without Reference Data

High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery

Contact Info

Product

Resources

About