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

doi:10.1016/j.agrformet.2020.108082

	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
	Chen, Yi 1,2; Tao, Fulu 1,2
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	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.
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关键词	WINTER-WHEAT YIELD ; TIME-SERIES ; SOLAR-RADIATION ; RISK-ASSESSMENT ; SATELLITE DATA ; CLIMATE-CHANGE ; KALMAN FILTER ; WOFOST MODEL ; MODIS-LAI ; INDEX
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000556177600038
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
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.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.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	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.