Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies
文献类型：期刊论文
| 作者 | Chen, Yi1,2; Tao, Fulu1,2
|
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2020-09-15 |
| 卷号 | 291页码:14 |
| 关键词 | Crop model Leaf area index (LAI) Remote sensing Data assimilation Yield estimation |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2020.108082 |
| 通讯作者 | Tao, Fulu(taofl@igsnrr.ac.cn) |
| 英文摘要 | Assimilating remote sensing data with crop growth model is a promising method to estimate crop yields over a large area. However, the method is always subject to the problems with biases in remote sensing products and assimilation weights in practical applications. In this study, we demonstrated the robustness of a 'spatial assimilation' method in dealing with the biases in three different remote sensing leaf area index (LAI) products. We further explored assimilation strategies for determining the assimilation weights when using 'spatial assimilation' method. Three different remote sensing LAI products were assimilated with MCWLA-Wheat model in the North China Plain during 2008-2015. The results demonstrated that the 'spatial assimilation' method was robust in mitigating the influences of biased LAI values and easily coupled with various LAI products based on different sources and retrieving algorithms. Furthermore, we found that the historical experiences of the optimal assimilation weights were not suitable to directly drive data assimilation in the coming seasons. Thus data-assimilation strategies to estimate crop yields without prior knowledge on the optimal assimilation weights were investigated. Two ensemble-mean-based assimilation strategies were recommended, which could reach 84 similar to 98% of yield estimation accuracy using the optimal assimilation weights. This study provides reliable and promising solutions for yield estimation over a large area using data assimilation without being limited by the biased state variables in remote sensing products and the lack of prior knowledge on the optimal assimilation weights. The 'spatial assimilation' method and the proposed ensemble-mean-based assimilation strategies have great potentials for wide applications, laying solid foundations for developing crop growth monitoring and yield forecasting system. |
| WOS关键词 | WINTER-WHEAT YIELD ; TIME-SERIES ; SOLAR-RADIATION ; RISK-ASSESSMENT ; SATELLITE DATA ; CLIMATE-CHANGE ; KALMAN FILTER ; WOFOST MODEL ; MODIS-LAI ; INDEX |
| 资助项目 | National Key Research and Development Program of China[2018YFA0606502] ; 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 ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000556177600038 |
| 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/311030/158202]  |
| 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 |
| 推荐引用方式 GB/T 7714 | Chen, Yi,Tao, Fulu. Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies[J]. AGRICULTURAL AND FOREST METEOROLOGY,2020,291:14. |
| APA | Chen, Yi,&Tao, Fulu.(2020).Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies.AGRICULTURAL AND FOREST METEOROLOGY,291,14. |
| MLA | Chen, Yi,et al."Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies".AGRICULTURAL AND FOREST METEOROLOGY 291(2020):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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