A Novel Fully Coupled Physical–Statistical–Deep Learning Method（一种新型的物理-统计-深度学习全耦合方法.pdf

remote sensing Article A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data Baoyu Du 1,2,†, Kebiao Mao 2,3,4, *,† , Sayed M. Bateni 5 , Fei Meng 1 , Xu-Ming Wang 3, Zhonghua Guo 3, Changhyun Jun 6 and Guoming Du 4 1 School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250100, China 2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China 3 School of Physics and Electronic-Engineering, Ningxia University, Yinchuan 750021, China 4 School of Public Administration and Law, Northeast Agricultural University, Harbin 150006, China 5 Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA 6 Department of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of Korea * Correspondence: maokebiao@ † These authors contributed equally to this work. Abstract: Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill- posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this