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Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

机译:利用佐治亚州小河实验流域的原位数据评估AMSR-E土壤湿度结果

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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, SX, 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. (C) 2008 Elsevier Inc. All rights reserved.
机译:目前,使用Njoku和Chan [Njoku,E.G.的描述]中的检索程序,根据NASA的Aqua卫星上的高级微波扫描辐射计(AMSR-E)的观测结果,生成了可操作的全球土壤湿度数据产品。和Chan,SX,2006.植被和表面粗糙度对AMSR-E陆地观测的影响,遥感环境,100(2),190-199]。我们使用不同的估算方法,使用陆地表面微波发射模型(LSMEM),从相同的AMSR-E观测到的亮度温度数据中生成了另一个土壤湿度数据集。本文着重比较了上述两种方法对土壤水分的估算。将来自当前AMSR-E产品和LSMEM的土壤水分数据与美国佐治亚州Little River试验流域(LREW)2003年的实地测量土壤水分数据集进行了比较。 AMSR-E白天和晚上的时间都超过了。在这项研究中,LSMEM方法的性能优于当前的AMSR-E操作算法。 AMSR-E和LSMEM结果之间的差异主要是由于对辐射传递方程,基于土壤和植被的物理模型中的变量进行了各种简化和假设的差异以及针对LSMEM正向模型输入的表面温度数据集的准确性方法。这项研究证实了遥感数据具有提供有用的水文信息的潜力,但是地球物理参数的准确性可能会根据估算方法而有所不同。从这项研究不能得出结论,通过LSMEM方法估算土壤湿度是否在其他地理,气候或地形条件下表现更好。然而,这项研究揭示了不同方法估算地球物理参数的效果,这可能对当前和未来的卫星飞行很有用。 (C)2008 Elsevier Inc.保留所有权利。

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