Recent studies have proposed that images from Synthetic Aperture Radar (SAR) sensors can be used to map spatially distributed soil moisture patterns within 5 cm of the surface. Unfortunately, many hydrologic applications require vadose zone soil moisture measurements rather than surface soil moisture measured by the SAR sensor. By combining SAR-derived surface soil moisture maps with a Soil-Vegetation-Atmosphere- Transfer (SVAT) model, it may be possible to obtain spatially distributed, temporally continuous information on vadose zone soil moisture. The first step in developing such a combined approach is to investigate the accuracy and precision of a SVAT model to estimate surface and vadose zone soil moisture over time. In this experiment, we evaluated the Simultaneous Heat and Water (SHAW) model by comparing its soil moisture estimates to a calibrated, one year, hourly soil moisture data set at three different depths under bare soil and shrub cover surfaces. Analysis indicated that the SHAW model overestimated soil moisture at each depth by an average of 0.02 M^3M&-3 under bare soil and underestimated soil moisture at each depth under shrub cover by an average of 0.02 m^3m^-3 . Based on this research, future studies should focus on calibration of the SHAW model and the assimilation of remotely sensed data as a primary model input.
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机译：最近的研究提出，合成孔径雷达（SAR）传感器的图像可用于在表面5 cm内绘制空间分布的土壤水分分布图。不幸的是，许多水文应用需要对渗流带土壤湿度进行测量，而不是由SAR传感器测量地表土壤湿度。通过将SAR衍生的地表土壤水分图与土壤-植被-大气转移（SVAT）模型相结合，可以获得渗流带土壤水分的时空分布信息。开发这种组合方法的第一步是研究SVAT模型的准确性和精密度，以估算随时间推移的表层和渗流带土壤湿度。在本实验中，我们通过将土壤水分估算值与裸土和灌木覆盖表面下三个不同深度处的校准的，一年一次的每小时土壤水分数据集进行比较，从而评估了同时供热和水分（SHAW）模型。分析表明，SHAW模型在裸土下每个深度平均高估了0.02 M ^ 3M＆-3，在灌木覆盖下每个深度平均低估了0.02 m ^ 3m ^ -3。基于此研究，未来的研究应侧重于SHAW模型的校准以及将遥感数据作为主要模型输入的同化。
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