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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness
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Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness

机译：融合微波和光学卫星观测以同时获取表层土壤水分，植被含水量和表层土壤粗糙度

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摘要

Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions.
机译：从被动微波观测中得出的表层土壤粗糙度的不确定性会大大降低表层土壤水分（SSM）和植被含水量（VWC）的性能。提出了一种融合微波和光学卫星观测方法客观确定辐射传递模型地表土壤粗糙度参数的算法。然后在半干旱原位观测站进行了证明。此新算法的粗糙度校正对SSM的性能（均方根误差从0.088降低到0.070）和从高级微波扫描辐射仪2和中分辨率成像光谱仪获得的VWC产生了积极影响。由于这种表面土壤粗糙度校正可以转移到其他微波卫星检索算法，例如土壤湿度和海洋盐度和土壤水分主动无源卫星，因此这种新算法可以为许多微波地球表面观测卫星任务做出贡献。

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