The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology
文献类型：期刊论文
| 作者 | Pei, Jie7,8; Tan, Shaofeng7; Zou, Yaopeng7; Liao, Chunhua7,8; He, Yinan9; Wang, Jian10; Huang, Huabing7,8; Wang, Tianxing7,8; Tian, Haifeng11; Fang, Huajun2,3 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2025-02-15 |
| 卷号 | 361页码:14 |
| 关键词 | Crop yield prediction Remote sensing Machine learning Temporal windows Food security |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2024.110340 |
| 产权排序 | 4 |
| 英文摘要 | Precise and timely crop yield predictions at large scales are crucial for safeguarding global food security. A key factor in accurate yield forecasting is the integration of multi-source environmental data, where the choice of time windows for aggregating these variables plays a pivotal role. Segmenting time windows by phenological stages allows for more precise extraction of environmental variables, capturing the complex interactions between crop development and external factors. However, the effectiveness of this approach in improving yield prediction accuracy, especially when comparing ground-based and remote sensing-derived land surface phenology data, remains largely unexplored. In this study, we investigate how phenology-based time windows affect corn yield predictions, using machine learning algorithms and multi-source environmental data from the U.S. Corn Belt. We systematically analyzed and compared models incorporating either ground-based or land surface phenology. By segmenting the growing season into six crucial stages (BBCH 00-99) and formulating specific yield forecasting models for each stage, we determined the optimal lead times for predictions utilizing both sources of phenological data. Our findings suggested that the incorporation of phenology-derived crop growth windows significantly enhances the accuracy of yield prediction by approximately 10 % compared to the fixed-season method. Ground-based phenology data from the USDA crop progress report slightly outperformed MODIS-based land surface phenology data, achieved the highest accuracy with the XGBoost model (R2 = 0.668, RMSE = 1.09 t/ha, MAE = 0.84 t/ha). Furthermore, this study demonstrated that reliable corn yield predictions could be made as early as the second phenological stage (Emerged-Silking for ground phenology, MidGreenup-Maturity for MODIS phenology). In regions where ground observations are limited or unavailable, land surface phenology emerges as a promising alternative. This study presents a robust framework for precise and extensive crop yield modeling and early prediction, which is crucial for making informed agricultural decisions and ensuring food security. |
| WOS关键词 | CORN GRAIN-YIELD ; TEMPERATURE ; MODEL ; VEGETATION ; MAIZE ; FORECASTS ; PATTERNS ; WHEAT ; BELT |
| 资助项目 | The Unveiling the List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[20222-051244] ; National Natural Science Foundation of China[42401575] ; National Natural Science Foundation of China[42101352] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110442] |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001372137400001 |
| 出版者 | ELSEVIER |
| 资助机构 | The Unveiling the List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone ; National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211692]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Liao, Chunhua; Huang, Jianxi |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 5.Southwest Jiaotong Univ, Fac Geosci & Engn, Chengdu 611756, Peoples R China 6.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China 7.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China 8.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop Ar, Zhuhai 519082, Peoples R China 9.Lawrence Berkeley Natl Lab, Earth & Environm Sci Area, 1 Cyclotron Rd, Berkeley, CA 94720 USA 10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pei, Jie,Tan, Shaofeng,Zou, Yaopeng,et al. The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology[J]. AGRICULTURAL AND FOREST METEOROLOGY,2025,361:14. |
| APA | Pei, Jie.,Tan, Shaofeng.,Zou, Yaopeng.,Liao, Chunhua.,He, Yinan.,...&Huang, Jianxi.(2025).The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology.AGRICULTURAL AND FOREST METEOROLOGY,361,14. |
| MLA | Pei, Jie,et al."The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology".AGRICULTURAL AND FOREST METEOROLOGY 361(2025):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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