Abstract The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced integral equation model and calibrated integral equation model were integrated to construct soil moisture retrieval scheme in combination with multi-sensor SAR observations. Through the above processing the influences of vegetation and surface roughness can be minimized in the developed soil moisture retrieval scheme. Finally, the least square based iteration approach was applied to derive soil moisture with multi-sensor SAR observations as inputs. Based on the field measurements and multi-sensor SAR data (C-band and X-band). acquired on June 2015 and October 2015 over Hebei agricultural areas, quantitative evaluation of the developed approach was implemented. The results indicate that accurate soil moisture was obtained by the developed approach over corn covered area and bare agricultural area. In comparison with the results obtained by LUT method, the least square based iteration approach achieved better estimations for soil moisture.

中文翻译：

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基于最小二乘法的多传感器数据农业土壤水分反演迭代方法

摘要 本研究的主要目的是利用多传感器遥感数据开发一种稳健的土壤水分反演方法。首先，利用水云模型消除植被对植被区SAR观测的影响，从而获得与土壤水分相关的裸土后向散射。然后，针对土壤水分反演欠定系统，结合多传感器SAR观测，将先进的积分方程模型和标定积分方程模型相结合，构建土壤水分反演方案。通过上述处理，可以在开发的土壤水分恢复方案中最大限度地减少植被和表面粗糙度的影响。最后，应用基于最小二乘法的迭代方法以多传感器 SAR 观测作为输入来推导土壤湿度。基于现场测量和多传感器 SAR 数据（C 波段和 X 波段）。分别于 2015 年 6 月和 2015 年 10 月收购了河北农区，对所开发的方法进行了定量评估。结果表明，所开发的方法在玉米覆盖区和裸露农业区获得了准确的土壤水分。与 LUT 方法获得的结果相比，基于最小二乘法的迭代方法实现了更好的土壤水分估计。结果表明，所开发的方法在玉米覆盖区和裸露农业区获得了准确的土壤水分。与 LUT 方法获得的结果相比，基于最小二乘法的迭代方法实现了更好的土壤水分估计。结果表明，所开发的方法在玉米覆盖区和裸露农业区获得了准确的土壤水分。与 LUT 方法获得的结果相比，基于最小二乘法的迭代方法实现了更好的土壤水分估计。