文献类型： 外文期刊

作者： Xiao, Guilong ¹ ; Huang, Jianxi ¹ ; Zhuo, Wen ⁴ ; Huang, Hai ⁵ ; Song, Jianjian ¹ ; Du, Kaiqi ¹ ; Wang, Jingwen ⁶ ; Yuan, Wenping ⁷ ; Sun, Liang ¹¹ ; Zeng, Yelu ¹² ; Su, Wei ¹ ; Wu, Genghong ¹⁵ ; Li, Xuecao ¹ ; Zheng, Juepeng ¹⁶ ; Miao, Shuangxi ¹ ; Gobin, Anne ¹⁷ ; Zhu, Peng ⁸ ; Jin, Zhenong ⁹ ;

作者机构： 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China

2.Southwest Jiao Tong Univ, Fac Geosci & Engn, Chengdu 611756, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100183, Peoples R China

4.Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China

5.Iowa State Univ, Ames, IA 50011 USA

6.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr Geospatial Informat, Shenzhen 518055, Peoples R China

7.Beijing Normal Univ, Beijing, Peoples R China

8.Sun Yat Sen Univ, Guangzhou, Peoples R China

9.Peking Univ, Inst Carbon Neutral, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100091, Peoples R China

10.Peking Univ, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100091, Peoples R China

11.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

12.Carnegie Inst Sci, Stanford, CA USA

13.Pacific Northwest Natl Lab, Joint Global Change Res Inst, College Pk, MD USA

14.Univ Wisconsin Madison, Dept Forest & Wildlife Ecol, Madison, WI USA

15.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Shaanxi, Peoples R China

16.Sun Yat Sen Univ, Sch Artificial Intelligence, Zhuhai 510275, Peoples R China

17.Flemish Inst Technol Res, Boeretang 200, B-2400 Mol, Belgium

18.Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium

19.Univ Hong Kong HKU, Dept Geog, Hong Kong 999077, Peoples R China

20.Univ Hong Kong HKU, Inst Climate & Carbon Neutral, Hong Kong 999077, Peoples R China

21.Univ Calif San Diego, La Jolla, CA USA

22.Lab Sci Climat & Environm, Gif Sur Yvette, France

23.AtlasAI, Luxembourg, Luxembourg

24.Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN 55455 USA

关键词： Crop yield; Remote sensing; Forecasting; Predictive models; Monitoring; Accuracy; Temperature sensors; Vegetation mapping; Stress; Photosynthesis

期刊名称：IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE （ 影响因子：16.4； 五年影响因子：19.1 ）

ISSN： 2473-2397

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Accurate and timely crop yield forecasts are critical to realizing global food security, balancing international grain trade, and promoting sustainable agricultural development. By providing consistent and large-scale observations, remote sensing technology has become indispensable in crop yield estimation across local, regional, and global scales. Over the past four decades, numerous crop yield forecasting approaches have been developed, including regression-based statistical models, machine learning, semi-empirical models, crop model-data assimilation (DA), and advanced deep learning (DL) approaches. This review comprehensively explores the latest advancements in these methodologies, critically evaluating their strengths and limitations in practical applications. In particular, this article highlights the challenges associated with spatiotemporal variability, environmental stress factors, and model scalability, offering potential solutions to enhance the accuracy and reliability of regional and global crop yield predictions. Besides, a selection strategy is also outlined, providing guidance on choosing the most appropriate yield estimation methods tailored to specific application objectives, data availability, and geographic scales. We also identify key factors affecting crop yield forecasting and offer insights into future trends and directions of development. Furthermore, we underscore the greatest potential of integrating artificial intelligence (AI) and remote sensing technologies with process-based crop growth models through DA techniques. This fusion holds significant promise for addressing the pressing need for accurate and scalable yield forecasts. As the global demand for food intensifies and the need for sustainable agriculture grows, the development and application of these advanced methodologies will be instrumental in ensuring resilient food systems and supporting sustainable agricultural practices.