TY - JOUR
T1 - Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia
AU - Sahoo, Alok K.
AU - Houser, Paul R.
AU - Ferguson, Craig
AU - Wood, Eric F.
AU - Dirmeyer, Paul A.
AU - Kafatos, Menas
N1 - Funding Information:
The valuable contributions made by the three anonymous reviewers to improve this manuscript are highly appreciated. We thank Dr. Eni Njoku and Dr. Steven Chan of NASA JPL for providing suggestions through personal communication to improve this manuscript. The LSMEM radiative transfer model was run at Princeton University. We thank Ming Pan for his help in providing the input datasets required for the LSMEM model and in running the LSMEM model. We are also grateful to Dr. Mike Cosh in USDA-ARS Lab at Beltsville, MD, USA for providing all the in-situ measurement datasets used in this study. Dr. Dirmeyer's participation was made possible under the Independent Research/Development provisions of NSF grant ATM-0610629. Craig Ferguson's and Eric Wood's participation were made possible through NASA grant NAG5-11111 and NASA/JPL contract 1281871.
PY - 2008/6/16
Y1 - 2008/6/16
N2 - An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.
AB - An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.
KW - AMSR-E
KW - In-situ observations
KW - LSMEM
KW - Little River Experimental Watershed
KW - Radiative transfer model
KW - Soil moisture
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U2 - 10.1016/j.rse.2008.03.007
DO - 10.1016/j.rse.2008.03.007
M3 - Article
AN - SCOPUS:44149120306
SN - 0034-4257
VL - 112
SP - 3142
EP - 3152
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 6
ER -