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浙江大学学报（农业与生命科学版）
Journal of Zhejiang University (Agriculture and Life Sciences)  2024, Vol. 50 Issue (2): 161-171    DOI: 10.3785/j.issn.1008-9209.2023.08.071
Reviews     
Research progress on crop yield prediction based on data assimilation system
Yu ZHAO1,2(),Wude YANG1(),Dandan DUAN2,Meichen FENG1,Chao WANG1
1.College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China
2.Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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Abstract  

Data assimilation system integrates the advantages of remote sensing data and crop growth models, providing a powerful means for real-time monitoring of agricultural production conditions. This paper, built upon a brief introduction to remote sensing methods for crop yield estimation, specifically focused on four aspects: the development of data assimilation algorithms, the application potential of multi-source remote sensing data for data assimilation, the uncertainty of data assimilation system, and the scale effects of data assimilation system. In addition, to address the current state of agricultural applications, future efforts should thoroughly explore the advantages of multi-source remote sensing data, multi-crop growth model ensembles, and data algorithms. The ultimate goal is to establish a crop yield estimation model centered around mechanism model, thereby providing robust data and technical support for the formulation of sound field management strategies, the planning of cereal industry layouts, and the establishment of import and export trade policies.

Key words： yield      data assimilation system      multi-crop growth model ensembles      multi-source remote sensing data     
Received: 07 August 2023      Published: 25 April 2024
CLC:  S127  
Corresponding Authors: Wude YANG     E-mail: zy928286257@163.com;sxauywd@126.com
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Yu ZHAO
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Cite this article:

Yu ZHAO, Wude YANG, Dandan DUAN, Meichen FENG, Chao WANG. Research progress on crop yield prediction based on data assimilation system. Journal of Zhejiang University (Agriculture and Life Sciences), 2024, 50(2): 161-171.

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https://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2023.08.071     OR     https://www.zjujournals.com/agr/Y2024/V50/I2/161


基于数据同化系统的作物产量预测研究进展

数据同化系统融合了遥感数据和作物生长模型的优势，是实时监测农业生产状况的有力手段。本文在简要介绍作物产量遥感估测方法的基础上，重点对数据同化算法的发展情况、多源遥感数据在数据同化上的应用潜力、数据同化系统的不确定性以及数据同化系统的尺度效应4方面进行论述。并且针对农业应用现状，提出未来应充分挖掘多源遥感数据、多作物生长模型集合和数据算法的优势，最终实现以机理模型为纽带的作物估产模式，并为制定田间管理策略、规划粮食产业布局和制定进出口贸易政策提供有力的数据和技术支撑。


关键词： 产量,  数据同化系统,  多作物生长模型集合,  多源遥感数据 
Fig. 1 Statistics of literatures on crop yield prediction based on remote sensing technology from 1975 to 2023A. Statistics of literature number; B. Word cloud. The statistical date was from 1975-01-01 to 2023-02-28.
Fig. 2 Remote sensing monitoring models for crop yieldSimple transformation model refers to semi-empirical model constructed by specific physiological process. In the formulas, Y, AP and RS represent yield, agronomic parameters and remote sensing data, respectively.
Fig. 3 Number of literatures on crop growth models from 2007 to 2023EPIC: Erosion-productivity impact calculator; MCWLA: Model to capture the crop-weather relationship over large area. The statistical date was from 2007-01-01 to 2023-02-28.

数据同化算法

Data assimilation algorithm

作物生长模型

Crop growth model

遥感数据

Remote sensing data

状态变量

State variable

文献

Reference

参数优化算法

Parameter

optimization

algorithm

单纯型搜索算法STICSSPOT、Landsat TM、机载影像叶面积指数[33]
复合型混合演化算法SAFYPlanet生物量[34]
复合型混合演化算法WOFOSTHJ-1A/B叶面积指数、叶片氮积累量[35]
模拟退火算法CERES-MaizeMODIS叶面积指数[36]
粒子群优化算法AquaCrop无人机数据生物量[13]
粒子群优化算法DSSAT地面高光谱数据叶面积指数、冠层氮积累量[37]
遗传算法SWAPLandsat ETM+蒸腾量[38]
Powell共轭方向法DSSATMODIS叶面积指数[39]
四维变分算法CERES-WheatSentinel叶面积指数、土壤含水量[40]

滤波算法

Filtering

algorithm

集合卡尔曼滤波算法SAFYLandsat叶面积指数[41]
集合卡尔曼滤波算法CERES-WheatSentinel叶面积指数、土壤含水量[40]
粒子滤波算法APSIMCubsat叶面积指数[42]
粒子滤波算法PILOTESentinel和Landsat叶面积指数[43]
Table 1 Main studies on data assimilation algorithm
Fig. 4 Schematic diagram of data assimilation algorithmA and B depict schematic diagrams of parameter optimization algorithm and filtering algorithm, respectively.
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