Estimating winter wheat biomass by coupling deep learning and hierarchical model using proximal remote sensing data
Year: 2025
Type: article
Language: en
Topic: Remote Sensing in Agriculture
Authors and affiliations:
raw_author_name: Weinan Chen
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China
raw_author_name: Guijun Yang
raw_affiliation_strings: Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China
raw_author_name: Aohua Tang
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China
raw_author_name: Jing Zhang
raw_affiliation_strings: State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China
raw_author_name: Hongrui Wen
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China
raw_author_name: Yang Meng
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
raw_author_name: Haikuan Feng
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
raw_author_name: Hao Yang
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
raw_author_name: Heli Li
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
raw_author_name: Xingang Xu
raw_affiliation_strings: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
raw_author_name: Changchun Li
raw_affiliation_strings: Institute of Quantitative Remote Sensing and Smart Agriculture, School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, China
raw_author_name: Zhenhong Li
raw_affiliation_strings: State Key Laboratory of Loess Science, College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, Shaanxi, China