Improving rice yield prediction with multi-modal UAV data:hyperspectral,thermal,and LiDAR integrationOA
Improving rice yield prediction with multi-modal UAV data:hyperspectral,thermal,and LiDAR integration
Shaofeng Tan;Jie Pei;Yaopeng Zou;Huajun Fang;Tianxing Wang;Jianxi Huang
School of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai,ChinaSchool of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai,China||Ministry of Education,Key Laboratory of Comprehensive Observation of Polar Environment,Sun Yat-sen University,Zhuhai,ChinaSchool of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai,ChinaKey Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,China||The Zhongke-Ji'an Institute for Eco-Environmental Sciences,Ji'an,ChinaSchool of Geospatial Engineering and Science,Sun Yat-sen University,Zhuhai,China||Ministry of Education,Key Laboratory of Comprehensive Observation of Polar Environment,Sun Yat-sen University,Zhuhai,ChinaFaculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu,China||College of Land Science and Technology,China Agricultural University,Beijing,China||Key Laboratory of Remote Sensing for Agri-Hazards,Ministry of Agriculture and Rural Affairs,Beijing,China
Unmanned Aerial Vehicles(UAV)crop yield predictionmulti-source datarice
Unmanned Aerial Vehicles(UAV)crop yield predictionmulti-source datarice
《地球空间信息科学学报（英文版）》 2026 (2)
中插2,788-807,21
This research is supported by"Unveiling the List of Hanging"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[Grant number 20222-051244]and the National Natural Science Foundation of China[Grant number 42401575].
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