In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations
文献类型：期刊论文
| 作者 | Lu, Chenxi6; Leng, Guoyong; Liao, Xiaoyong5; Tu, Haiyang6; Qiu, Jiali; Li, Ji; Huang, Shengzhi4; Peng, Jian2,3 |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2024-11-15 |
| 卷号 | 358页码:110242 |
| 关键词 | In-season maize yield prediction Seasonal climate forecasts Satellite-based vegetation index Random forest Northeast China |
| DOI | 10.1016/j.agrformet.2024.110242 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Various yield forecasting methods have been reported in literature, but the benefits of assimilating seasonal climate forecasts and satellite observations for in-season yield forecasting during different growth stages have rarely been examined using machine learning. By synthesizing census yields, seasonal climate forecasts (SCF) and satellite-based gross primary production (GPP), this study develops a machine learning (i.e., Random Forest)-based in-season maize yield forecasting model for Northeast China, which produces over 40 % of China's total maize production. Based on the dynamically trained model, 11 numerical experiments are conducted to investigate the benefits of assimilating SCF and GPP relative to the experiments without using SCF and GPP for yield prediction in four forecast phases (planting-three leaves, three leaves-jointing, jointing-tasseling, tasseling-milk). Generally, our yield forecasting model exhibits a promising skill with a low bias of 4.11 %-4.97 %, when the observed climate and GPP are used as inputs. When climate forecasts are assimilated into the model by sampling historical climate, satisfactory yield forecasting can be achieved one month before harvest with a bias of 5.50 %-5.63 %. Bias-correcting climate forecast data from dynamical weather forecast models has a larger benefit for yield prediction with a lower bias of 4.77 %-5.06 %. Furthermore, we found a better benefit of assimilating SCF when compared with GPP during the first three forecast phases, although its relative importance decreases substantially towards harvest. Finally, phase-dependent maps indicating the best model are produced for each county, with historical resampling methods performing best in 40 %, 32 %, 27 %, and 21 % of counties from Phase 1 to Phase 4 and dynamical weather forecast models showing greater predictability in 54 %, 58 %, 58 %, and 52 % of counties during Phases 1-4, respectively. This study provides a useful yield forecasting framework for the breadbasket of China, which can be extended to other crops. |
| WOS关键词 | BIAS CORRECTION ; CROP ; MODEL ; PRECIPITATION ; TEMPERATURE ; WHEAT ; RICE ; CORN |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001322692500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207913]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Leng, Guoyong |
| 作者单位 | 1.Univ Leipzig, Remote Sensing Ctr Earth Syst Res RSC4Earth, D-04103 Leipzig, Germany 2.UFZ Helmholtz Ctr Environm Res, Dept Remote Sensing, Permoserstr 15, D-04318 Leipzig, Germany 3.Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, Xian, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Chenxi,Leng, Guoyong,Liao, Xiaoyong,et al. In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations[J]. AGRICULTURAL AND FOREST METEOROLOGY,2024,358:110242. |
| APA | Lu, Chenxi.,Leng, Guoyong.,Liao, Xiaoyong.,Tu, Haiyang.,Qiu, Jiali.,...&Peng, Jian.(2024).In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations.AGRICULTURAL AND FOREST METEOROLOGY,358,110242. |
| MLA | Lu, Chenxi,et al."In-season maize yield prediction in Northeast China: The phase-dependent benefits of assimilating climate forecast and satellite observations".AGRICULTURAL AND FOREST METEOROLOGY 358(2024):110242. |
入库方式： OAI收割
来源：地理科学与资源研究所
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