Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area
文献类型：期刊论文
| 作者 | Chen, Yi1,2; Tao, Fulu1,2,3 |
| 刊名 | FIELD CROPS RESEARCH
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 276页码:11 |
| 关键词 | Crop model Remote sensing Data assimilation Food security Yield forecasting |
| ISSN号 | 0378-4290 |
| DOI | 10.1016/j.fcr.2021.108398 |
| 通讯作者 | Tao, Fulu(taofl@igsnrr.ac.cn) |
| 英文摘要 | Regional crop yield forecasting before harvest is critical for managing climate risk, optimizing agronomic management, and making food trade policy. The advantage of remote sensing data-crop model assimilation for yield estimates has been well recognized, however its potential for early-season crop yield forecasting has not yet been investigated. In this study, combining a crop model, remote sensing leaf area index (LAI) assimilation and weather forecasts, we conducted yield forecasting for winter wheat in the central North China Plain during 2008-2015. Sequential forecasting was conducted to assess the yield forecasting potential and the effects of LAI assimilation and multiple weather forecasts with different lead times. Results showed that forecasting skill increased with shortening of lead time. Assimilating remote sensing LAI into crop model was valuable and critical to improve yield forecasting skills. The uncertainties from weather forecasts could weaken the forecasting skills; and using historical weather observations performed better than using weather forecasts outputted by climate models. In general, winter wheat yield in the central North China Plain could be reliably forecasted with a lead time from five weeks (mean MAPE < 10%, ROC score > 0.8, ACC> 0.65) to two months (mean MAPE <12%, ROC score> 0.75, ACC > 0.55) before harvest. The study highlights that current available data can provide fair yield forecasting; nevertheless the remote sensing LAI data and weather forecasts need further improvements to improve yield forecasting skills and provide valuable suggestions for stakeholders to respond to the forecasts timely. |
| WOS关键词 | WINTER-WHEAT YIELD ; ESSENTIAL CLIMATE VARIABLES ; TIME-SERIES ; KALMAN FILTER ; CERES-MAIZE ; GEOV1 LAI ; INDEX ; PREDICTION ; CORN ; LANDSAT |
| 资助项目 | National Key Research and Development Program of China[2018YFA0606502] ; National Key Research and Development Program of China[2019YFA0607401] ; National Natural Science Foundation of China[41901127] ; National Natural Science Foundation of China[31761143006] ; National Natural Science Foundation of China[41571493] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000742588200005 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169657]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tao, Fulu |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Nat Resources Inst Finland Luke, Helsinki 00790, Finland |
| 推荐引用方式 GB/T 7714 | Chen, Yi,Tao, Fulu. Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area[J]. FIELD CROPS RESEARCH,2022,276:11. |
| APA | Chen, Yi,&Tao, Fulu.(2022).Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area.FIELD CROPS RESEARCH,276,11. |
| MLA | Chen, Yi,et al."Potential of remote sensing data-crop model assimilation and seasonal weather forecasts for early-season crop yield forecasting over a large area".FIELD CROPS RESEARCH 276(2022):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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