Knowledge Resource Center for Ecological Environment in Arid Area
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
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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). |
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