干旱区生态环境知识资源中心(Arid): Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model

Arid

DOI	10.3390/rs14133194
	Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model
	Zhang, Yuxin; Huang, Jianxi; Huang, Hai; Li, Xuecao; Jin, Yunxiang; Guo, Hao; Feng, Quanlong; Zhao, Yuanyuan
通讯作者	Huang, JX
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:13
英文摘要	Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST2 (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST2 and BMGV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland.
英文关键词	grassland aboveground biomass data assimilation 4DVar four-dimensional variational MCMC Markov chain Monte Carlo ModVege model
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000824050500001
WOS关键词	MANAGED PERMANENT PASTURES ; LEAF-AREA INDEX ; PREDICTING DYNAMICS ; INNER-MONGOLIA ; GROWTH-MODELS ; MODIS DATA ; HERBAGE ; DIGESTIBILITY ; PRODUCTIVITY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394164
推荐引用方式 GB/T 7714	Zhang, Yuxin,Huang, Jianxi,Huang, Hai,et al. Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model[J],2022,14(13).
APA	Zhang, Yuxin.,Huang, Jianxi.,Huang, Hai.,Li, Xuecao.,Jin, Yunxiang.,...&Zhao, Yuanyuan.(2022).Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model.REMOTE SENSING,14(13).
MLA	Zhang, Yuxin,et al."Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model".REMOTE SENSING 14.13(2022).