دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model
المؤلفون: Yuxin Zhang, Jianxi Huang, Hai Huang, Xuecao Li, Yunxiang Jin, Hao Guo, Quanlong Feng, Yuanyuan Zhao
المصدر: Remote Sensing, Vol 14, Iss 13, p 3194 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Science
مصطلحات موضوعية: grassland aboveground biomass, data assimilation, 4DVar, four-dimensional variational, MCMC, Markov chain Monte Carlo, Science
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2072-4292
Relation: https://www.mdpi.com/2072-4292/14/13/3194; https://doaj.org/toc/2072-4292
DOI: 10.3390/rs14133194
URL الوصول: https://doaj.org/article/9bdd01bc09d043bd85205db9a4d631db
رقم الأكسشن: edsdoj.9bdd01bc09d043bd85205db9a4d631db
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20724292
DOI:10.3390/rs14133194