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Root-zone soil moisture estimation based on remote sensing data and deep learning.
A, Yinglan; Wang, Guoqiang; Hu, Peng; Lai, Xiaoying; Xue, Baolin; Fang, Qingqing.
  • A Y; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences
  • Wang G; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing, 100875, China; Water Engineering and Management, Asian Institute of Technology, Pathum Thani, 12120, Thailand. Electronic address: wanggq@bnu.edu.cn.
  • Hu P; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
  • Lai X; College of Management and Economics, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China.
  • Xue B; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing, 100875, China.
  • Fang Q; School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China. Electronic address: fangqingqing@ncepu.edu.cn.
Environ Res ; 212(Pt B): 113278, 2022 09.
Article en En | MEDLINE | ID: mdl-35430274
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Suelo / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Suelo / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article