干旱区生态环境知识资源中心(Arid): Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data

Arid

DOI	10.1016/j.agwat.2022.107583
	Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data
	Jin, Ning; Tao, Bo; Ren, Wei; He, Liang; Zhang, Dongyan; Wang, Dacheng; Yu, Qiang
通讯作者	He, L
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2022
卷号	266
英文摘要	Irrigation plays an important role in crop yield production in arid and semi-arid regions. However, irrigation effects have not been well addressed in the application of crop models at a regional scale due to limited availability, which constrains the reliability and accuracy of simulation results. Assimilating remote sensing information into crop models can provide a viable approach to reduce associated uncertainties. In this study, regional irrigation data for winter wheat (Triticum aestivum L.) grown on the Loess Plateau was used to calibrate and validate the ChinaAgrosys (China Agricultural System) crop model at site and regional scales. Remote sensing data was then assimilated into the ChinaAgrosys crop model under four assimilation schemes. remotely sensed assimilation state variables (i.e., LAI and NDVI) and two assimilation algorithms (i.e., (Partical swarm optimization) and SCE-UA (Shuffled complex evolution)) were considered. During the winter wheat growing season on the Loess Plateau, 30.6% of the wheat production area was irrigated once, 6.7% irrigated two times, 3.7% was irrigated three times, and the remaining wheat area was rainfed. The R2 values between maturity date, LAI, and yield simulated by the ChinaAgrosys crop model and observations agrometeorological stations on the Loess Plateau were greater than 0.73, 0.44, and 0.60, respectively, during 2010-2015. The accuracy and spatial heterogeneity of winter wheat yield estimation were effectively improved by assimilating remote sensing data into the ChinaAgrosys crop model based on regional irrigation data. the four assimilation schemes, the combination of PSO+NDVI produced the highest accuracy for yield estimation in Hongtong county (92.8%), followed by SCE-UA+NDVI (92.0%). Our results demonstrated the importance accounting for the spatial heterogeneity of water availability when applying a crop model in arid and semi-arid regions. Additionally, our analysis regarding different assimilation state variables and algorithms indicated both simulation accuracy and calculation efficiency should be considered when assimilating remote sensing
英文关键词	Data assimilation Remote sensing data ChinaAgrosys crop model Regional irrigation Winter wheat
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000793224400003
WOS关键词	LEAF-AREA INDEX ; WATER-USE EFFICIENCY ; RAIN-FED WHEAT ; VEGETATION INDEXES ; KALMAN FILTER ; WOFOST MODEL ; TIME-SERIES ; MODIS ; SIMULATION ; PARAMETERS
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391600
推荐引用方式 GB/T 7714	Jin, Ning,Tao, Bo,Ren, Wei,et al. Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data[J],2022,266.
APA	Jin, Ning.,Tao, Bo.,Ren, Wei.,He, Liang.,Zhang, Dongyan.,...&Yu, Qiang.(2022).Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data.AGRICULTURAL WATER MANAGEMENT,266.
MLA	Jin, Ning,et al."Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data".AGRICULTURAL WATER MANAGEMENT 266(2022).