由于地表粗糙度和植被等因素的影响，使得利用合成孔径雷达（SAR）观测来反演土壤水分仍是具有挑战性的工作。本项目拟利用多波段、多时相的SAR观测，协同可见光影像，主要开展三方面的研究：1）尝试采用时间序列的SAR观测和改进地表粗糙度的表达（以及与之相应的半经验模型）两种手段来消减粗糙度的影响，提高裸土状况下的土壤水分反演精度；2）利用光学遥感观测，分析比较不同植被参数在微波辐射传输模型中的适用性，从而更好的校正植被效应，改进土壤水分反演；3）将结合地面点观测、宇宙射线观测系统和模型模拟，对反演结果开展多尺度的真实性检验，从而更系统全面的度量反演结果的误差和不确定性。研究总体目标为发展新的土壤水分反演策略/方法，使之能更有效的对地表粗糙度和植被影响加以校正，进一步增强不同波段SAR数据土壤水分反演的可靠性和稳健性；以及依靠地面观测和模型模拟，发展针对SAR反演土壤水分的多尺度真实性检验方法。

Microwave remote sensing is a commonly used technique to obtain spatially distributed soil moisture information. Although many efforts have been made over decades, it is still challenging to retrieve soil moisture by using synthetic aperture radar (SAR) observations, due to strong impact caused by surface roughness and vegetation effect. Upon this background, this study proposes to use multi-temporal, multi-band SAR observations at L-, C- and X-band, in combination with optical imagery, to carry out three mian reseach contents: 1) two stategies will be investigated and further developed to eliminate the impact of surface roughness, i.e. through combined roughness parameter (and its related semi-empirical forward model) and multi-temporal imagery, to improve the accuracy of soil moisture estimaties in bare soil condition ; 2) considering the vegetation effect, we will explore which vegetaion parameter/index is optimal to be used in microwave radiative transfer model as vegetation description factors, and how to better acquire these factors at the pixel-scale in asscociation with optical remote senisng data ; 3) to perform multi-scale validation dependent on in situ measurements at three scales, from point and the Cosmic-Ray Neutron Probe (CRNP) and model simulations, to better quantify the error and uncertainty of retrievals. On this basis, the overall goal of this study is to evaluate multiply existing retrieval methods and to further develop them, in order to build a new retrieval stategy/method that can be less dependent on ancillary data and better deal with surface roughness and vegetation effect. Besides, methods for multi-scale validation of SAR retrieved soil moisture will be investigated, which can enhance our understanding of remote sensing product validaiton in terms of mulit-source observations across scales.