现有的被动微波地表温度与土壤湿度独立反演互相制约，且对辅助数据依赖性高，导致反演精度低。针对这一问题，本项目充分利用被动微波多频率双极化的特性，基于微波辐射传输理论，开展地表温度和土壤湿度一体化物理反演研究。首先，将土壤、植被和大气辐射模型耦合，构建土壤-植被-大气全链路微波辐射传输模型，生成不同地表和大气条件下的被动微波模拟数据库。然后，基于被动微波模拟数据库，从微波辐射传输方程出发，充分发挥地表温度和土壤湿度在一体化反演方程中互为补充的优势，通过物理数学公式推导对方程中各项进行近似合并，构建仅与频率相关但与极化无关的方程项，进一步忽略数量级较小的方程项，有效减少方程的未知数个数。联立多通道方程，建立不依赖大气透过率、植被透过率和地表粗糙度信息的地表温度和土壤湿度一体化反演模型，实现地表温度和土壤湿度的高精度同时反演。最后，利用现有产品数据和野外测量数据对模型反演结果进行精度评价。

The existing land surface temperature and soil moisture separate retrieval algorithms from passive microwave observations are mutually constrained and have high dependence on auxiliary data. This leads to low retrieval accuracy. To solve this problem, this project makes full use of the advantages of passive microwave sensors which have multi-frequency and dual polarization, and aims to perform the study of land surface temperature and soil moisture simultaneous retrieval based on the passive microwave radiative transfer theory. First, the soil, vegetation and atmospheric radiative transfer models are coupled to construct a soil-vegetation-atmospheric integrated radiative transfer model. A passive microwave simulated database which considers different surface and atmospheric conditions is generated based on the integrated radiative transfer model. Then, taking full use of the complementary advantages of land surface temperature and soil moisture in the radiative transfer equation and using the passive microwave simulated database, a term that are only frequency dependent but not related to polarization is constructed and further a term with lesser magnitude is ignored through the physical mathematics formula derivation. This can reduce the number of unknowns of the radiative transfer equation effectively. Using equations from multiple frequency and dual polarization, a simultaneous retrieval model of land surface temperature and soil moisture, which is independent of atmospheric transmittance, vegetation transmittance, and surface roughness information, is constructed. This can achieve the land surface temperature and soil moisture simultaneously with high accuracy. Finally, the accuracy of retrieval results is evaluated using existing product data and field measurement.