数字农科院2.0

Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model

文献类型： 外文期刊

作者： Yuxin Zhang;Jianxi Huang;Hai Huang;Xuecao Li;Yunxiang Jin;Hao Guo;Quanlong Feng;Yuanyuan Zhao

作者机构：

关键词： 4DVar;data assimilation;four-dimensional variational;grassland aboveground biomass;Markov chain Monte Carlo;MCMC;ModVege model

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2022 年 14.0 卷 13 期

页码：

摘要： Grassland aboveground biomass is crucial for evaluating grassland desertification, deg-radation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, espe-cially 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 bio-mass, 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.

分类号：

  • 相关文献

[1]Integrating remote sensing assimilation and SCE-UA to construct a grid-by-grid spatialized crop model can dramatically improve winter wheat yield estimate accuracy. Qiang Li,Maofang Gao,Sibo Duan,Guijun Yang,Zhao Liang Li. 2024

[2]Estimation of regional crop yield by assimilating multi-temporal TM images into crop growth model. Yang, Peng,Zhou, Qingbo,Chen, Zhongxin,Zha, Yan,Wu, Wenbin,Shibasaki, Ryosuke. 2006

[3]Assimilating MODIS-LAI into Crop Growth Model with EnKF to Predict Regional Crop Yield. Huang, Jianxi,Ma, Guannan,Wu, Sijie,Liu, Xingquan,Fan, Jinlong,Zou, Jinqiu. 2012

[4]Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation. Huang, Jianxi,Ma, Hongyuan,Su, Wei,Zhang, Xiaodong,Huang, Jianxi,Ma, Hongyuan,Su, Wei,Zhang, Xiaodong,Huang, Yanbo,Fan, Jinlong,Wu, Wenbin. 2015

[5]Application of Crop Model Data Assimilation With a Particle Filter for Estimating Regional Winter Wheat Yields. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Liu, Jia,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Li, He. 2014

[6]Crop Model Data Assimilation with Particle Filter for Yield Prediction Using Leaf Area Index of Different Temporal Scales. Li, He,Chen, Zhongxin,Wu, Wenbin,Liu, Bin,Hasi, Tuya,Li, He,Chen, Zhongxin,Wu, Wenbin,Liu, Bin,Hasi, Tuya. 2015

[7]ASSIMILATION OF FIELD MEASURED LAI INTO CROP GROWTH MODEL BASED ON SCE-UA OPTIMIZATION ALGORITHM. Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Du, Yunyan,Qin, Jun. 2009

[8]The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang. 2014

[9]Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Ma, Guannan,Huang, Jianxi,Wu, Wenbin,Zou, Jinqiu,Fan, Jinlong,Wu, Sijie.

[10]Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Huang, Jianxi,Tian, Liyan,Ma, Hongyuan,Su, Wei,Zhang, Xiaodong,Zhu, Dehai,Liang, Shunlin,Liang, Shunlin,Becker-Reshef, Inbal,Huang, Yanbo,Wu, Wenbin.

[11]Data assimilation for crop yield and CO2 fixation monitoring in Asia by a photosynthetic sterility model using satellites and meteorological data. Kaneko, Daijiro,Kumakura, Toshiro,Yang, Peng. 2009

[12]INTEGRATING REMOTELY SENSED LAI WITH EPIC MODEL BASED ON GLOBAL OPTIMIZATION ALGORITHM FOR REGIONAL CROP YIELD ASSESSMENT. Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Tang, Huajun,Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Tang, Huajun,Qin, Jun. 2010

[13]A review of data assimilation of crop growth simulation based on remote sensing information. Jiang Zhiwei,Chen Zhongxin,Liu Jia,Sun Liang,Jiang Zhiwei,Chen Zhongxin,Liu Jia,Sun Liang. 2014

[14]Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved from Networked Optical and SAR Remotely Sensed Images into the WOFOST Model. Shangrong Wu,Jianqiang Ren,Zhongxin Chen,Peng Yang,He Li,Jia Liu. 2021

[15]Optimizing duration of incubation experiments for understanding soil carbon decomposition. Guan X.,Jiang J.,Jing X.,Feng W.,Luo Z.,Wang Y.,Xu X.,Luo Y.. 2022

[16]Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model. Jianxi Huang,Jianjian Song,Hai Huang,Wen Zhuo,Quandi Niu,Shangrong Wu,Han Ma,Shunlin Liang. 2024

[17]A review of remote sensing estimation of crop water productivity: definition, methodology, scale, and evaluation. Minghan Cheng,Dameng Yin,Wenbin Wu,Ningbo Cui,Chenwei Nie,Lei Shi,Shuaibing Liu,Xun Yu,Yi Bai,Yadong Liu,Yuqin Zhu,Xiuliang Jin. 2023

[18]Improving crop yield estimation by unified model parameters and state variable with Bayesian inference. Jianjian Song,Jianxi Huang,Hai Huang,Guilong Xiao,Xuecao Li,Li Li,Wei Su,Wenbin Wu,Peng Yang,Shunlin Liang. 2024

[19]A Review of the Research Status and Prospects of Regional Crop Yield Simulations. Rongkun Zhao,Yujing Ma,Shangrong Wu. 2024

[20]Trends in crop yield estimation via data assimilation based on multi-interdisciplinary analysis. Hong Cao,Rongkun Zhao,Lang Xia,Shangrong Wu,Peng Yang. 2025

作者其他论文 更多>>