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

2021 ◽  
Vol 15 (10) ◽  
pp. 4975-4980
Author(s):  
Zacharie Barrou Dumont ◽  
Simon Gascoin ◽  
Olivier Hagolle ◽  
Michaël Ablain ◽  
Rémi Jugier ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Real Time ◽  
Snow Depth ◽  
Communication Evaluation ◽  
The Cost ◽  
Monitoring Service ◽  
Sentinel 2 ◽  
Good Agreement ◽  
Snow And Ice

Abstract. The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near-real-time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy (proportion of correct classifications) of 94 % and kappa of 0.81. More accurate (+6 % kappa) retrievals are obtained by excluding low-quality pixels at the cost of a reduced coverage (−13 % data).

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

2021 ◽  
Author(s):  
Zacharie Barrou Dumont ◽  
Simon Gascoin ◽  
Olivier Hagolle ◽  
Michaël Ablain ◽  
Rémi Jugier ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Real Time ◽  
Snow Depth ◽  
Communication Evaluation ◽  
The Cost ◽  
Monitoring Service ◽  
Sentinel 2 ◽  
Good Agreement ◽  
Snow And Ice

Abstract. The High Resolution Snow & Ice Monitoring Service was launched in 2020 to provide near real time, pan-European snow and ice information at 20 m resolution from Sentinel-2 observations. Here we present an evaluation of the snow detection using a database of snow depth observations from 1764 stations across Europe over the hydrological year 2016–2017. We find a good agreement between both datasets with an accuracy of 94 % (proportion of correct classifications) and kappa of 0.80. More accurate (+6 % kappa) retrievals are obtained by excluding low quality pixels at the cost of a reduced coverage (−13 % data).

scholarly journals Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2018 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

The CMEMS High Resolution Coastal Service

2021 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Kerstin Stelzer ◽  
Martin Böttcher ◽  
João Felipe Cardoso dos Santos ◽  
Carole Lebreton ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Landsat 8 ◽  
Backscatter Coefficient ◽  
Ocean Colour ◽  
New Production ◽  
In Situ Data ◽  
Full Picture ◽  
Spm Concentration ◽  
Sentinel 2

<p>High-quality satellite-based ocean colour products can provide valuable support and insights in management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including traditional medium resolution ocean colour systems (e.g. SeaWiFS, MODIS-AQUA, MERIS, Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI, Landsat-8/OLI, Pleiades) and geostationary satellites (e.g. SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics.</p><p>As a new production unit, the high resolution coastal service will be integrated in CMEMS. It offers 12 different products which are covered within the Ocean Colour Thematic Assembly Centre (OCTAC). The products can be categorized in two groups: 1) near real time (NRT) and Multi-Year near real time (MYNRT). The products are generated the coastal waters (20km stripe for the coastline) for all European Seas and are provided in 100m spatial resolution. All products are based on Sentinel-2 MSI data. The primary OCTAC variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral Remote Sensing Reflectance (RRS). This, together with the Particulate Backscatter Coefficient (BBP), constitute the category of the optics products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm. The transparency products include turbidity (TUR) and Suspended Particulate Matter (SPM) concentration. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions. The geophysical product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging. The NRT products are generally provided withing 24 hours after end of the acquisition day, while monthly averaged products are provided few days after end of the respective month. A third group of products are daily gap-filled products which are provided once in a quarter. Validation of the variables has been performed by match-up analysis with in situ data as well as by comparison of the high resolution products with the well established Low Resolution CMEMS Ocean Colour products. The products will be introduced in the CMEMS service by May 2021. We will present the products themselves as well as the validation results for the different variables. The known limitations will be reported in order to provide a full picture of the new service.</p>

scholarly journals Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies

2020 ◽  
Vol 24 (1) ◽  
pp. 143-157 ◽  
Author(s):  
Michael Schirmer ◽  
John W. Pomeroy
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Spatial Correlation ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Initial Distribution ◽  
Aerial Photographs ◽  
Slope Aspect ◽  
Spatial Correlations ◽  
The North

Abstract. The spatial distribution of snow water equivalent (SWE) and melt are important for estimating areal melt rates and snow-cover depletion (SCD) dynamics but are rarely measured in detail during the late ablation period. This study contributes results from high-resolution observations made using large numbers of sequential aerial photographs taken from an unmanned aerial vehicle on an alpine ridge in the Fortress Mountain Snow Laboratory in the Canadian Rocky Mountains from May to July in 2015. Using structure-from-motion and thresholding techniques, spatial maps of snow depth, snow cover and differences in snow depth (dHS) during ablation were generated in very high resolution as proxies for spatial SWE, spatial ablation rates and SCD. The results indicate that the initial distribution of snow depth was highly variable due to overwinter snow redistribution; thus, the subsequent distribution of dHS was also variable due to albedo, slope/aspect and other unaccountable differences. However, the initial distribution of snow depth was 5 times more variable than that of the subsequent dHS values, which varied by a factor of 2 between the north and south aspects. dHS patterns were somewhat spatially persistent over time but had an insubstantial impact on SCD curves, which were overwhelmingly governed by the initial distribution of snow depth. The reason for this is that only a weak spatial correlation developed between the initial snow depth and dHS. Previous research has shown that spatial correlations between SWE and ablation rates can strongly influence SCD curves. Reasons for the lack of a correlation in this study area were analysed and a generalisation to other regions was discussed. The following questions were posed: what is needed for a large spatial correlation between initial snow depth and dHS? When should snow depth and dHS be taken into account to correctly model SCD? The findings of this study suggest that hydrological and atmospheric models need to incorporate realistic distributions of SWE, melt energy and cold content; therefore, they must account for realistic correlations (i.e. not too large or too small) between SWE and melt in order to accurately model SCD.

Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration 

2021 ◽  
Author(s):  
Florentin Hofmeister ◽  
Leonardo F. Arias-Rodriguez ◽  
Marco Borga ◽  
Valentina Premier ◽  
Carlo Marin ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Model Calibration ◽  
Data Availability ◽  
Cloud Detection ◽  
Snow Storage ◽  
Elevation Zone ◽  
Snow Model ◽  
Sentinel 2

<p>Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.</p>

scholarly journals The NOAA FM-CW Snow-Level Radar

2017 ◽  
Vol 34 (2) ◽  
pp. 249-267 ◽  
Author(s):  
Paul E. Johnston ◽  
James R. Jordan ◽  
Allen B. White ◽  
David A. Carter ◽  
David M. Costa ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Vertical Structure ◽  
Continuous Wave ◽  
High Resolution Data ◽  
The Cost ◽  
Precipitating Clouds ◽  
Resolution Data

AbstractA vertically pointing radar for monitoring radar brightband height (BBH) has been developed. This new radar utilizes frequency-modulated continuous wave (FM-CW) techniques to provide high-resolution data at a fraction of the cost of comparable pulsed radars. This S-band radar provides details of the vertical structure of precipitating clouds, with full Doppler information. Details of the radar design are presented along with observations from one storm. Results from a calibration using these storm data show the radar meets the design goals. Eleven of these radars have been deployed and provide BBH data in near–real time.

scholarly journals Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2019 ◽  
Vol 11 (2) ◽  
pp. 493-514 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Covered Area ◽  
Covered Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed on average 5 d after the image acquisition as raster and vector files via the Theia portal (https://doi.org/10.24400/329360/F7Q52MNK).

scholarly journals Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 129 ◽  
Author(s):  
Gaia Piazzi ◽  
Cemal Tanis ◽  
Semih Kuter ◽  
Burak Simsek ◽  
Silvia Puca ◽  
...  
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Large Scale ◽  
Complex Topography ◽  
Snow Properties ◽  
High Resolution Imagery ◽  
Wide Range ◽  
Moderate Resolution ◽  
Sentinel 2

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances.

scholarly journals High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area

2020 ◽  
Vol 12 (3) ◽  
pp. 460 ◽  
Author(s):  
Mingyu Liu ◽  
Chuan Xiong ◽  
Jinmei Pan ◽  
Tianxing Wang ◽  
Jiancheng Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Mountainous Areas ◽  
Snow Water ◽  
Sentinel 2

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.

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