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

Gascoin, Simon; Grizonnet, Manuel; Bouchet, Marine; Salgues, Germain; Hagolle, Olivier

doi:10.5194/essd-11-493-2019

Cited by 178 publications

(166 citation statements)

References 29 publications

Supporting

Mentioning

161

Contrasting

Order By: Relevance

“…The World Meteorological Organization has defined snow albedo and snow extent as essential climate variables (ECVs) that must be monitored on a daily basis globally using satellite observations. The retrieval of snow optical properties is currently done using various space-borne instrumentation including optical sensors such as MODerate-resolution Imaging Spectroradiometer (MODIS) [5][6][7][8][9][10][11][12], GLobal Imager (GLI) [13,14], MEdium Resolution Imaging Spectrometer(MERIS) [15], Visible Infrared Imaging Radiometer Suite (VIIRS) [16], MultiSpectral Imager (MSI)/Sentinel-2 (S-2) [17], etc.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

Kokhanovsky¹,

et al. 2019

View full text Add to dashboard Cite

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

show abstract

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

Kokhanovsky¹,

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In particular there is a mismatch between the spatial scale of field data and the current model extent and resolution which prevents a robust estimation of the model error. To further evaluate the model ability to resolve small-scale variability, higher resolution remote sensing products like Sentinel-2 SCA (Lebanon has been recently included in Theia Snow collection, Gascoin et al, 2019) or stereo-satellite snow depth (Marti et al, 2016) should be useful. Another important limitation of this work is the short record of the forcing data from the AWS that does not allow drawing robust conclusions on the interannual variability of the SWE in Mount Lebanon and even less on the extremes.…”

Section: Resultsmentioning

confidence: 99%

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Fayad¹,

Gascoin²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

show abstract

“…Likewise, [18] conducted an extensive MODIS validation in Austria, reporting low MODIS accuracy, evaluated by means of in-situ snow depth observations, from November to March in forest areas. We should also take into account that most of the forest in the study catchment is evergreen, which is particularly challenging for MODIS snow detection due to a combination of two factors: i) it is not clear how well canopy-intercepted snow is detected by optical sensors [40,41], and ii) the density of the canopy hampers remote snow detection underneath forest [23,42].…”

Section: Satellite-derived and Simulated Snow Cover Area Comparisonmentioning

confidence: 99%

Comparison of MODIS and Model-Derived Snow-Covered Areas: Impact of Land Use and Solar Illumination Conditions

et al. 2020

View full text Add to dashboard Cite

Moderate resolution imaging spectroradiometry (MODIS) snow cover accuracy has been assessed in the past at different scales, with various approaches and in relation to the many factors influencing the remote observation of snow-covered areas (SCA). However, the challenge of fully characterizing MODIS accuracy over forest sites is still open. In this study, we exploit 5 years of data from the upper river Adige basin at Ponte Adige (Eastern Italian Alps) to condition an enhanced temperature index snowpack model accounting for model parameter uncertainty by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The simulated SCA is then compared with MODIS retrievals through a range of different statistical metrics to investigate how land use and solar illumination conditions affect such comparison. In particular, the Overall Accuracy index (OA) is used to quantify the agreement between satellite-derived and simulated SCA on a pixel-by-pixel basis. Analyzing the spatial variability either of the median OA and its range shows that illumination conditions over forested canopies represent a major source of uncertainty in MODIS SCA. Exploiting this finding, we identify the minimum level of incoming short-wave radiation for accurate use of MODIS SCA in forest areas.

show abstract

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

Cited by 178 publications

References 29 publications

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Comparison of MODIS and Model-Derived Snow-Covered Areas: Impact of Land Use and Solar Illumination Conditions

Contact Info

Product

Resources

About