中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 千烟洲站森林生态系统研究中心
  5. 外文论文
Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration

文献类型：期刊论文

作者Tan, Shaofeng7; Pei, Jie1,7; Zou, Yaopeng7; Fang, Huajun5,6; Wang, Tianxing1,7; Huang, Jianxi2,3,4
刊名GEO-SPATIAL INFORMATION SCIENCE
出版日期2025-07-26
卷号N/A
关键词Unmanned Aerial Vehicles (UAV) crop yield prediction multi-source data rice
ISSN号1009-5020
DOI10.1080/10095020.2025.2535524
产权排序3
文献子类Article ; Early Access
英文摘要Rice yield prediction is critical for ensuring food security, particularly in major rice-producing countries like China. While Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imaging are widely used for yield prediction due to its ability to capture detailed spectral information, they may not fully account for key factors such as canopy structure and moisture content. To improve accuracy, integrating Thermal Infrared (TIR) data, which reflects canopy moisture, and Light Detection and Ranging (LiDAR) data, which provides crop height and canopy density, is essential. However, the role of biophysical features provided by these sensors in yield prediction across different phenological stages remains unclear. This study addresses this gap by evaluating the combined use of hyperspectral, TIR, and LiDAR data collected during key rice growth stages: tillering, booting, heading, and filling. Two key questions were explored: (1) Does integrating multi-modal data at multiple phenological stages consistently improve yield prediction accuracy? (2) What is the optimal phenological stage for accurate rice yield prediction at the sub-field scale? Multi-modal information, including 2D/3D spectral indices, textural features, temperature data, and canopy structural attributes, was derived and integrated for rice yield prediction using ensemble Machine Learning (ML) models. Single-temporal and multi-temporal modeling strategies were compared. Results showed that hyperspectral data alone achieved satisfactory accuracy during the booting stage (R2 = 0.806), mainly driven by 2D texture and 3D spectral features. Combining TIR-derived temperature features and LiDAR-derived structural features did not improve early-stage predictions but significantly enhanced accuracy during mid-to-late stages, particularly at heading. The highest prediction accuracy (R2 = 0.837) was achieved using a multi-stage model combining data from the tillering, booting, and heading stages. This study provides valuable insights into optimizing sensor fusion strategies and identifying the most informative growth stages for rice yield prediction.
URL标识查看原文
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001536028500001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/215553]  
专题千烟洲站森林生态系统研究中心_外文论文
通讯作者Pei, Jie
作者单位1.Sun Yat sen Univ, Key Lab Comprehens Observat Polar Environm, Minist Educ, Zhuhai, Peoples R China;
2.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing, Peoples R China
3.China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China;
4.Southwest Jiaotong Univ, Fac Geosci & Engn, Chengdu, Peoples R China;
5.Zhongke Jian Inst Ecoenvironm Sci, Jian, Peoples R China;
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China;
7.Sun Yat sen Univ, Sch Geospatial Engn & Sci, Zhuhai, Peoples R China;
推荐引用方式
GB/T 7714		 Tan, Shaofeng,Pei, Jie,Zou, Yaopeng,et al. Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration[J]. GEO-SPATIAL INFORMATION SCIENCE,2025,N/A.
APA Tan, Shaofeng,Pei, Jie,Zou, Yaopeng,Fang, Huajun,Wang, Tianxing,&Huang, Jianxi.(2025).Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration.GEO-SPATIAL INFORMATION SCIENCE,N/A.
MLA Tan, Shaofeng,et al."Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration".GEO-SPATIAL INFORMATION SCIENCE N/A(2025).

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。