scholarly journals ESTIMATION OF WATER USAGE IN THE SHIGUAN RIVER BASIN, CHINA USING SATELLITE REMOTE SENSING BASED ON FIELD SURVEY AND LAND SURFACE SCHEME

2007 ◽  
Vol 51 ◽  
pp. 211-216
Author(s):  
Osamu KOZAN ◽  
Kenji TANAKA ◽  
Shuichi IKEBUCHI
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Field Survey ◽  
Water Usage ◽  
Land Surface Scheme ◽  
Surface Scheme

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

2018 ◽  
Vol 10 (11) ◽  
pp. 1703
Author(s):  
Nicolas Marchand ◽  
Alain Royer ◽  
Gerhard Krinner ◽  
Alexandre Roy ◽  
Alexandre Langlois ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Conductivity ◽  
Soil Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Thermal State ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Land Surface Scheme ◽  
Surface Scheme

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.

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