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Snow-Covered Soil Temperature Retrieval in Canadian Arctic Permafrost Areas, Using a Land Surface Scheme Informed with Satellite Remote Sensing Data

1
Centre d’Applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
2
Centre for Northern Studies, Québec, QC G1A, Canada
3
Institut des Géosciences de l’Environnement (IGE), CNRS, Univ. Grenoble Alpes, 38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Actual Position: Département des Sciences de l’Environnement, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada.
Remote Sens. 2018, 10(11), 1703; https://doi.org/10.3390/rs10111703
Received: 25 September 2018 / Revised: 20 October 2018 / Accepted: 25 October 2018 / Published: 29 October 2018
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Abstract

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow’s geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution. View Full-Text
Keywords: soil temperature; permafrost; passive microwave; thermal infrared; snow cover; Land Surface Model; Radiative Transfer Model; Canadian arctic soil temperature; permafrost; passive microwave; thermal infrared; snow cover; Land Surface Model; Radiative Transfer Model; Canadian arctic
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Marchand, N.; Royer, A.; Krinner, G.; Roy, A.; Langlois, A.; Vargel, C. Snow-Covered Soil Temperature Retrieval in Canadian Arctic Permafrost Areas, Using a Land Surface Scheme Informed with Satellite Remote Sensing Data. Remote Sens. 2018, 10, 1703.

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