Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data

doi:10.1016/j.eja.2018.09.006

	Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data
	Chen, Yi 1,2; Zhang, Zhao 2; Tao, Fulu 1,3
刊名	EUROPEAN JOURNAL OF AGRONOMY
	2018-11-01
卷号	101 页码:163-173
关键词	Winter wheat Leaf area index (LAI) Spatial differences Data assimilation MCWLA-Wheat model Yield estimation
ISSN号	1161-0301
DOI	10.1016/j.eja.2018.09.006
通讯作者	Tao, Fulu(taofl@igsnrr.ac.cn)
英文摘要	Crop yield estimation at regional scale using crop model is generally subjected to large uncertainties from insufficient spatial information on heterogeneous growth environment and agronomic management practices. To solve this problem, we assimilated crop phenology and leaf area index (LAI) derived from remote sensing into a crop model (MCWLA-Wheat) to improve its reliability in estimating winter wheat yields at regional scale. Since the LAI magnitude was obviously underestimated however its spatial pattern was relatively well captured by remote sensing, we developed a novel spatial assimilation scheme that assimilated the spatial differences instead of the absolute values of LAI into crop model. Firstly, we retrieved the information of critical development stages of winter wheat from remote sensing data to adjust the simulation of phenology by MCWLA-Wheat model; then the spatial differences of LAI derived from remote sensing were assimilated into the MCWLA-Wheat model using a kind of constant gain Kalman Filter algorithm to improve the ability of the model in estimating winter wheat LAI and yields at regional scale in the North China Plain. This assimilation scheme extracted effective information from remote sensing LAI and meanwhile abandoned the information with obvious errors, ensuring that the assimilation variables could be close to the reality. It avoids the requirement for correction of the LAI derived from remote sensing using other high-quality ancillary data from field measurements. Using this assimilation scheme, the performance of crop model improved substantially. It successfully produced more accurate yield estimates at regional scale during the period of 2001-2008 (mean R-2 = 0.42, RMSE = 737/ha) than those without assimilation (mean R-2 = 0.26, RMSE = 1012 kg/ha) and those directly assimilating the absolute LAI values derived from remote sensing (mean R-2 = 0.30, RMSE = 1257/ha). Our findings demonstrated a reliable and promising assimilation scheme for improving yield estimation of crop model at regional scale with low data requirement.
资助项目	National Key Research and Development Program of China[2017YFD0300301] ; National Key Research and Development Program of China[2016YFD0300201] ; National Natural Science Foundation of China[31761143006] ; National Natural Science Foundation of China[31561143003] ; National Natural Science Foundation of China[41571493] ; National Natural Science Foundation of China[41571088]
WOS关键词	ENSEMBLE KALMAN FILTER ; CROP GROWTH-MODEL ; TIME-SERIES ; SATELLITE DATA ; MODIS DATA ; STOMATAL CONDUCTANCE ; WOFOST MODEL ; MAIZE YIELD ; LAI ; INFORMATION
WOS研究方向	Agriculture
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000452942600017
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/311030/51407]
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, Key Lab Environm Change & Nat Hazards, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yi,Zhang, Zhao,Tao, Fulu. Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data[J]. EUROPEAN JOURNAL OF AGRONOMY,2018,101:163-173.
APA	Chen, Yi,Zhang, Zhao,&Tao, Fulu.(2018).Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data.EUROPEAN JOURNAL OF AGRONOMY,101,163-173.
MLA	Chen, Yi,et al."Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data".EUROPEAN JOURNAL OF AGRONOMY 101(2018):163-173.