Green vegetation biomass estimation in Mongolia using RS data and machine-learning methodsing methods
Year: 2026
Type: article
Abstract: Green vegetation biomass is a key ecological indicator reflecting ecosystem productivity, rangeland condition, and responses to climate variability, particularly in arid and semi-arid environments. In Mongolia, where vast grassland and desert-steppe ecosystems support both biodiversity and pastoral livelihoods, spatially explicit biomass information is essential for ecological monitoring and conservation-oriented land management. Traditional field-based biomass measurements, however, are labor-intensive and limited in spatial coverage. In this study, we estimated aboveground biomass of green vegetation across Mongolia using moderate-resolution remote sensing data and machine learning methods to support large-scale ecological assessment. Three spectral bands and nine vegetation indices derived from Moderate Resolution Imaging Spectroradiometer MOD13A1 data from August 2021 were combined with field measurements from 275 monitoring sites. Random forest, artificial neural network, and extreme gradient boosting \ models were trained and optimized using grid-search hyperparameter tuning and evaluated through cross-validation. Among the tested models, Random forest achieved the highest predictive accuracy (R² = 0.88, MAE = 68.45 kg ha⁻¹, RMSE = 109.93 kg ha⁻¹), followed by extreme gradient boosting (R² = 0.80, MAE = 87.47 kg ha⁻¹, RMSE = 143.09 kg ha⁻¹), while artificial neural network showed comparatively lower performance. The resulting biomass maps reveal pronounced spatial variability linked to Mongolia’s climatic and ecological gradients. Our findings demonstrate that machine learning methods combined with remote sensing provide an efficient framework for estimating vegetation biomass across Mongolia under the environmental conditions represented by the August 2021 dataset. The generated biomass products offer valuable ecological insights and can support rangeland monitoring, conservation planning, and assessments of ecosystem vulnerability under ongoing climate change.
Source: Mongolian Geoscientist
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7213304014