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Passive microwave remote sensing of surface soil moisture: Methods, results, and applications.

机译:表面土壤水分的无源微波遥感:方法,结果和应用。

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This study investigates passive microwave remote sensing of surface soil moisture from three aspects: methods, results, and applications. A land surface microwave emission model (LSMEM) has been implemented to retrieve soil moistures from various remote sensing data. Chapter 2 introduces the physics and parameterization of the LSMEM algorithm. Based on this framework, soil moisture is estimated from L band synthetic radiometry during the Southern Great Plains 1999 experiment. Results show a RMS of 1.8-2.8% volumetric soil moisture. To conduct operational retrievals from spaceborne radiometry, LSMEM is further parameterized at large scales in Chapter 3. A five-year (1998-2002) surface soil moisture product is derived across the southern United States from TRMM/TMI X band horizontally polarized brightness temperatures. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The product is validated by Oklahoma Mesonet field measurements and its spatial patterns also demonstrate consistencies with precipitation fields. This is the first time that a fully validated approach and product is implemented and the product is to be made available through the NASA Goddard Space Flight Center Distributed Active Archive Center (NASA/GSFC DAAC). Chapter 4 explores the application potential of assimilating spaceborne remote sensing soil moisture product into land surface models through the development of "observational operators." Besides the TMI product, soil moisture from AMSR-E using the operational NASA retrieval algorithm is involved. The focus is to use Copula, a probabilistic approach, to generate observation operators so that the systematic bias between remotely sensed and modeled soil moisture can be reduced and the error structure can be provided for generation of assimilation ensembles. Observation operators are derived from different remote sensing products for two land surface models: Variable Infiltration Capacity (VIC) and ECMWF reanalysis (ERA40).
机译:这项研究从三个方面研究了被动微波对表层土壤水分的遥感:方法,结果和应用。已经实施了陆面微波发射模型(LSMEM),以从各种遥感数据中检索土壤水分。第2章介绍了LSMEM算法的物理原理和参数化。在此框架的基础上,根据1999年南部大平原地区实验的L波段合成辐射法估算了土壤湿度。结果表明,土壤水分的RMS为1.8-2.8%。为了从星载辐射测量中进行操作性检索,在第3章中进一步对LSMEM进行了大规模参数化。从TRMM / TMI X波段的水平极化亮度温度中,得出了美国南部五年(1998-2002年)的地表土壤水分产物。由于在以茂密植被,活跃降水,积雪和冻土为特征的区域,在观测到的亮度温度下,土壤水分的信息含量有限,因此提供了用于回收土壤水分的质量控制标志。该产品已通过俄克拉荷马州Mesonet野外测量进行了验证,其空间格局也证明了与降水场的一致性。这是首次实施经过完全验证的方法和产品,并且将通过NASA Goddard太空飞行中心分布式主动档案中心(NASA / GSFC DAAC)提供该产品。第四章通过开发“观测算子”,探索将星载遥感土壤水分产品同化为地表模型的应用潜力。除TMI产品外,还涉及使用可操作的NASA检索算法从AMSR-E获得的土壤水分。重点是使用概率方法Copula来生成观测算子,以便可以减少遥感和模拟土壤水分之间的系统偏差,并可以为同化集合的生成提供误差结构。观测员来自不同的遥感产品,用于两种陆地表面模型:可变渗透能力(VIC)和ECMWF再分析(ERA40)。

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