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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Microwave and Meteorological Fusion: A method of Spatial Downscaling of Remotely Sensed Soil Moisture
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Microwave and Meteorological Fusion: A method of Spatial Downscaling of Remotely Sensed Soil Moisture

机译：微波和气象融合：一种遥感土壤湿度的空间缩小方法

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摘要

Downscaling of microwave remotely sensed that soil moisture content (SMC) is an efficient way to obtain spatial continuous SMC at a finer resolution. However, the classical optical/thermal and microwave fusion, and the active and passive microwave fusion cannot work under all-weather conditions because of contamination of clouds or the lack of suitable radar data source. In this study, a microwave and meteorological fusion (MMF) is provided. The MMF method is based on a complementary relationship hypothesis assuming SMC is reflected in the adjacent surface atmospheric moisture under midday conditions. By this method, daily passive SMC products from Soil Moisture Active Passive (SMAP) mission with 36-km resolution were disaggregated using a daily gridded meteorological data with nominal 4-km resolution. The original and downscaled SMCs were evaluated by comparing with in situ SMC obtained from three core validation sites and three sparse networks. The experiment was conducted in the central part of the U.S. from April 2015 to June 2018. Results demonstrated that the downscaled SMC maintained the dynamic range of original SMC product and energy was conserved. Furthermore, the downscaled SMC showed good agreement with and slightly outperformed the original SMC as compared with in situ SMC. The downscaling method is shown to capture higher resolution SMC spatial variability while preserving the quality of original SMC. However, because of the complexity of soil moisture-atmosphere interactions, the actual contributing domain of downscaled SMC may be greater than 4 km. The MMF method is suggested as a supplementary for all-weather downscaling coarse-resolution SMC.
机译：微波缩小的远程感测到土壤水分含量（SMC）是在更精细的分辨率下获得空间连续SMC的有效方法。然而，经典的光学/热和微波融合，以及主动和被动微波融合由于云污染或缺乏合适的雷达数据源而无法在全天候条件下工作。在该研究中，提供了微波和气象融合（MMF）。 MMF方法基于互补关系假设，假设SMC在午间条件下反映在相邻的表面大气水分中。通过这种方法，具有36公里分辨率的土壤湿度主动（SMAP）任务的每日被动SMC产品通过具有标称4公里分辨率的每日网格气象数据分解。通过与三个核心验证站点和三个稀疏网络中获得的原位SMC进行比较来评估原始和较次级SMC。该实验是在美国2015年4月至2018年6月的中部进行的。结果表明，较低的SMC保持了原始SMC产品的动态范围和节约能源。此外，与原位SMC相比，较低的SMC与原始SMC相比表现出良好的协议。缩小方法被示出为捕获更高的分辨率SMC空间可变性，同时保留原始SMC的质量。然而，由于土壤水分 - 气氛相互作用的复杂性，较次级SMC的实际贡献领域可能大于4公里。建议MMF方法作为全天候挖掘粗辨率SMC的辅助。

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