1Estimate of Cloudy-Sky Surface Emissivity From Passive Microwave Satellite Data Using Machine Learning

作者： Zhu X.-M.; Song X.-N.; Leng P.; Li Z.-L.; Li X.-T.; Gao L.; Guo D.; SCIE（2022版） EI（2022版）

机构： University of the Chinese Academy of Sciences; College of Resources and Environment; Beijing; 100049; China; ICube (UMR 7357); UdS; Cnrs; Illkirch-Graffenstaden; 67412; France; University of Chinese Academy of Sciences; Yanshan Earth Critical Zone and Surface Fluxes Research Station; 101408; Ministry of Agriculture; Institute of Agricultural Resources and Regional Planning; Chinese Academy of Agricultural Sciences; Key Laboratory of Agricultural Remote Sensing; 100081; Chinese Institute of Water Resource and Hydropower Research; 100038;

来源：IEEE Transactions on Geoscience and Remote Sensing

关键词： Atmospheric temperature Decision trees Error analysis Learning systems Microwave measurement Microwave sensors Microwaves Radiometers Remote sensing Surface measurement All-weather AMSR-E product Atmospheric modeling Cloud-computing Land surface Maximum-likelihood estimation Microwave imaging Microwave land surface emissivity Microwave radiometry Random forests Surface emissivity Maximum likelihood estimation AMSR-E clear sky climate conditions emissivity error analysis estimation method hydrometeorology machine learning MODIS radiometer satellite data

年份：2022