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Development of soil moisture profiles through coupled microwave-thermal infrared observations in the southeastern United States

机译:通过耦合微波 - 热红外线观测在美国东南部的土壤湿度型材的发展

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The principle of maximum entropy (POME) can be used to develop vertical soil moisture (SM) profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based SM estimates from the Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmospheric Land EXchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse-resolution (similar to 25 km) microwave SM estimates to match the finer resolution (similar to 5 km) thermal data. The study was conducted over multiple years (2006-2010) in the southeastern US. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (LSM), as well as in situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square differences (ubRMSD) in the range of 0.01-0.09m(3) m(-3). The profile results at SCAN sites showed a mean bias of 0.03 and 0.05 (m(3) m(-3)); ubRMSD of 0.05 and 0.06 (m(3) m(-3)); and correlation coefficient of 0.44 and 0.48 against SCAN observa-tions and Noah LSM, respectively. Correlations were generally highest in agricultural areas where values in the 0.6-0.7 range were achieved.
机译:最大熵(POME)的原理可用于开发垂直土壤水分(SM)型材。 PAME模型所需的最小输入使其成为遥感应用的绝佳选择。 Pome模型的两个主要输入要求是表面边界条件和型材平均水分含量。基于微波的SM估计来自先进的微波扫描辐射计(AMSR-E)可以提供表面边界条件,而来自大气陆地交换逆(Alexi)表面能平模型的热红外水分可以提供平均水分状况。遵循分解方法,遵循低档粗分辨率(类似于25km)微波SM估计,以匹配更精细的分辨率(类似于5km)热数据。该研究在美国东南部多年(2006-2010)进行。将分列的土壤水分估计与发达的型材相比,与诺亚陆地表面模型(LSM)以及来自10个天然资源保护服务(NRC)土壤气候分析网络(扫描)地点的原位测量到学习区域内。扫描站点的总分解结果表明,在大多数情况下,分解在0.01-0.09m(3)m(-3)范围内改善了与无偏的根均方差(Ubrmsd)的时间相关性。扫描位点的轮廓结果显示为0.03和0.05(m(3)m(-3))的平均偏差; ubrmsd为0.05和0.06(m(3)m(-3));分别对扫描观察和诺亚LSM的相关系数0.44和0.48。在达到0.6-0.7范围内的数值的农业区域中的相关性通常是最高的。

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