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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:一种遥感土壤水分的空间缩减方法

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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:微波的缩小遥感发现土壤水分含量(SMC)是一种以较高分辨率获得空间连续SMC的有效方法。但是,由于云的污染或缺乏合适的雷达数据源,经典的光/热和微波融合以及有源和无源微波融合无法在全天候条件下工作。在这项研究中,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在中午条件下会反射到相邻的地面大气水分中。通过这种方法,使用标称4公里分辨率的每日网格化气象数据,对36公里分辨率的土壤水分主动被动(SMAP)任务的每日被动SMC产品进行分类。通过与从三个核心验证站点和三个稀疏网络获得的原位SMC进行比较,对原始和缩小规模的SMC进行了评估。该实验于2015年4月至2018年6月在美国中部进行。结果表明,缩小后的SMC可以保持原始SMC产品的动态范围,并且可以节省能源。此外,与原位SMC相比,缩小后的SMC与原始SMC表现出良好的一致性,并且略胜于原始SMC。缩减方法显示出可捕获更高分辨率的SMC空间变异性,同时保留了原始SMC的质量。但是,由于土壤水分与大气相互作用的复杂性,按比例缩小的SMC的实际贡献范围可能大于4 km。建议使用MMF方法作为全天候按比例缩小粗分辨率SMC的补充。

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