文献类型： 外文期刊

作者： Mao, Kebiao ¹ ; Wang, Han ¹ ; Shi, Jiancheng ⁵ ; Heggy, Essam ⁶ ; Wu, Shengli ⁸ ; Bateni, Sayed M. M. ⁹ ; Du, Guoming ¹¹ ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Hulunber Grassland Ecosyst Natl Observat & Res Stn, Beijing 100081, Peoples R China

2.Ningxia Univ, Sch Phys & Elect Engn, Yinchuan 750021, Peoples R China

3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

4.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250100, Peoples R China

5.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China

6.Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90089 USA

7.CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA

8.Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China

9.Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI 96822 USA

10.Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI 96822 USA

11.Northeast Agr Univ, Sch Publ Adm & Law, Harbin 150006, Peoples R China

关键词： deep learning; geophysical logical reasoning; interleaved iterative optimization; soil moisture; land surface temperature; collaborative retrieval

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 7 期

页码：

收录情况： SCI

摘要： Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrieval accuracy needs to be improved. In this study, a novel fully-coupled paradigm is developed to robustly retrieve SM and LST from passive microwave data, which integrates deep learning, physical methods, and statistical methods. The key condition of the general paradigm proposed by us is that the output parameters of deep learning can be uniquely determined by the input parameters theoretically through a certain mathematical equation. Firstly, the physical method is deduced based on the energy radiation balance equation. The nine unknowns require the brightness temperatures of nine channels to construct nine equations, and the solutions of the physical method equations are obtained by model simulation. Based on the derivation of the physical method, the solution of the statistical method is constructed using multi-source data. Secondly, the solutions of physical and statistical methods constitute the training and test data of deep learning, which is used to obtain the solution curve of physical and statistical methods. The retrieval accuracy of LST and SM is greatly improved by smartly utilizing the mutual prior knowledge of SM and LST and cross iterative optimization calculations. Finally, validation indicates that the mean absolute error of the retrieved SM and LST data are 0.027 m(3)/m(3) and 1.38 K, respectively, at an incidence angle of 0-65 degrees. A model-data-knowledge-driven and deep learning method can overcome the shortcomings of traditional methods and provide a paradigm for retrieval of other geophysical variables. The proposed paradigm not only has physical meaning, but also makes deep learning physically interpretable, which is a milestone in the retrieval of geophysical remote sensing parameters based on artificial intelligence technology.