Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data

doi:10.1016/j.agrformet.2020.107993

	Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data
	Zhang, Zhao 2,3; Li, Ziyue 2,3; Chen, Yi 1,4; Zhang, Lingyan 2,3; Tao, Fulu 1,4
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2020-08-15
卷号	290 页码:13
关键词	Climate variability Model optimization Data assimilation MCWLA-Wheat Remote sensing Soil moisture
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2020.107993
通讯作者	Tao, Fulu(taofl@igsnrr.ac.cn)
英文摘要	Assimilating multi-source data into crop models is a promising way to improve crop growth simulations and yield estimations over a large area. Most of previous studies have mainly assimilated one of the observed/retrieved variables such as leaf area index (LAI) or soil moisture. However, assimilating multi-source data into a model and evaluating their respective contributions to improvements in model simulations have been rare. In this study, we proposed a novel Multi-Step-Assimilating of a crop model with Multi-source Data (MSAcmMD) and further demonstrated it with the MCWLA-Wheat model in improving the simulations of crop development, soil moisture dynamics, and grain yield for winter wheat in the North China Plain. The MSAcmMD, based on the calibrating assimilation strategy, followed the logical links among sub-modules of the crop model. It includes four assimilation steps: (i) calibrating crop model parameters; (ii) assimilating crop phenology; (iii) assimilating soil moisture; and (iv) assimilating crop LAI. The results showed that MSAcmMD can improve substantially the simulations of crop development, soil moisture dynamics, grain yields, and their spatiotemporal patterns over a large area and during a relative long-term period. During 2001-2008, across the study areas, the coefficient of determination (R-2) of the simulated yields was increased from 0.39 to 0.75, and root-mean-square-error (RMSE) was reduced from 1096 to 467 kg/ha, relative to the initial model estimates. An additional validation for the year of 2009 further substantiated the robustness of MSAcmMD, with average R-2 of 0.65 and RMSE of 500 kg/ ha. Further analyses showed that assimilation of soil moisture contributed most to the improvement of yield estimations, with R-2 increasing by 43% and RMSE reducing by 47%. Our findings demonstrated a reliable and promising assimilation system in improving crop growth simulations and yield predictions over a large area. MSAcmMD and the related methods provided a large potential in applying to other crops and regions.
资助项目	National Natural Science Foundation of China[41571493] ; National Natural Science Foundation of China[31761143006] ; National Key Research & Development Program of China[2017YFA0604703] ; National Key Research & Development Program of China[2019YFA0607401] ; National Key Research & Development Program of China[2016YFD0300201] ; State Key Laboratory of Earth Surface Processes and Resources Ecology
WOS关键词	LEAF-AREA INDEX ; WINTER-WHEAT ; SOIL-MOISTURE ; TIME-SERIES ; CLIMATE-CHANGE ; KALMAN FILTER ; WOFOST MODEL ; LAI PRODUCTS ; LOWLAND RICE ; MODIS-LAI
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000540172000006
资助机构	National Natural Science Foundation of China ; National Key Research & Development Program of China ; State Key Laboratory of Earth Surface Processes and Resources Ecology
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162224]
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.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, MoE Key Lab Environm Change & Nat Hazards, Beijing 100875, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhao,Li, Ziyue,Chen, Yi,et al. Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data[J]. AGRICULTURAL AND FOREST METEOROLOGY,2020,290:13.
APA	Zhang, Zhao,Li, Ziyue,Chen, Yi,Zhang, Lingyan,&Tao, Fulu.(2020).Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data.AGRICULTURAL AND FOREST METEOROLOGY,290,13.
MLA	Zhang, Zhao,et al."Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data".AGRICULTURAL AND FOREST METEOROLOGY 290(2020):13.