Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations
Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations
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DOI：
10.1016/j.rse.2015.11.022
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发表时间：
2016-07-01
影响因子：
13.5
通讯作者：
Ducharne, A.
中科院分区：
文献类型：
--
作者：
Al-Yaari, A.;Wigneron, J. P.;Ducharne, A.
在欧洲空间局（欧空局）努力开发最一致和最完整的表层土壤水分（SSM）记录的框架内，本研究调查了一种统计方法，从星载观测中检索全球和长期的SSM数据集。更具体地说，这项研究调查的能力，基于物理的统计回归检索SSM从两个被动微波遥感观测：先进的微波扫描辐射计（AMSR-E; 2003年9月。2011年）和土壤水分和海洋盐度（SMOS）卫星。使用AMSR-E水平和垂直亮温（TB）观测值和SMOS 3级SSM（SMOSL 3;作为训练数据集）校准回归系数。该校准过程在2010年6月至2010年9月期间进行。2011年期间，在这两个SMOS和AMSR-E观测重合。基于这些校准系数，从2003-2011年期间的AMSR-E TB观测计算出全球SSM产品（此处称为AMSR-reg）。通过在校准期间评价AMSR-reg SSM产品与SMOSL 3 SSM产品的相关性（R）和均方根误差（RMSE），评估回归质量。AMSR-reg和SMOSL 3 SSM产品之间的一致性很好（平均全球R = 0.60，平均全球RMSE = 0.057 m（3）/m（3）），特别是在澳大利亚，美国中部，中亚和萨赫勒地区。在第二步中，AMSR-reg SSM检索和常用的AMSR-E SSM检索来自土地参数检索模型（AMSR-LPRM），进行了评估，对两种SSM参考（i）全球MERRA-土地SSM模拟和（ii）在2003-2009年的原位测量。结果表明，AMSR-reg和AMSR-LPRM（在考虑全局模拟时更好）都成功捕获了具有可比相关值的参考的时间动态。在无偏RMSE（ubRMSE）方面，AMSR-reg与MERRA-land比AMSR-LPRM更一致，AMSR-reg的ubRMSE全球平均值为0.055 m（3）/m（3），AMSRLPRM为0.084 m（3）/m（3）。总之，统计回归，这是第一次使用长期的星载TB数据集进行测试，似乎是一个很有前途的方法，从被动微波遥感TB观测检索SSM。（C）2015 Elsevier Inc. All rights reserved.
Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003-Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010-Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003-2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m(3)/m(3)), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003-2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m(3)/m(3) for AMSR-reg and 0.084 m(3)/m(3) for AMSRLPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations. (C)2015 Elsevier Inc. All rights reserved.