Green vegetation biomass estimation in Mongolia using RS data and machine-learning methodsing methods

Dandinsuren Amarsaikhan; Dalantai Sainbayar; Enkhtuya Jargaldalai; Enkhmanlai Amarsaikhan; Gendaram Odontuya; Enkhjargal Egshiglen; Damdin Enkhjargal; Tsedev Bat-Erdene

doi:10.5564/mgs.v31i62.5291

Authors

Dandinsuren Amarsaikhan Division of Aerospace Remote Sensing, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 14190, Mongolia https://orcid.org/0000-0002-4715-6518
Dalantai Sainbayar Division of Aerospace Remote Sensing, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 14190, Mongolia https://orcid.org/0000-0001-8806-6167
Enkhtuya Jargaldalai Division of Aerospace Remote Sensing, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 14190, Mongolia https://orcid.org/0000-0002-2790-3539
Enkhmanlai Amarsaikhan Oyunii Tsomorlig Impex LLC, Ulaanbaatar 14200, Mongolia https://orcid.org/0009-0007-6279-9857
Gendaram Odontuya Department of Pharmaceutical Chemistry and Pharmacognosy, Mongolian University of Pharmaceutical Science, Ulaanbaatar 18130, Mongolia https://orcid.org/0000-0002-4924-0932
Enkhjargal Egshiglen Oyunii Tsomorlig Impex LLC, Ulaanbaatar 14200, Mongolia https://orcid.org/0009-0008-4162-2590
Damdin Enkhjargal Division of Aerospace Remote Sensing, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 14190, Mongolia https://orcid.org/0009-0005-2556-3285
Tsedev Bat-Erdene Department of Physical Geography, Mongolian State University of Education, Ulaanbaatar 14191, Mongolia https://orcid.org/0009-0004-9735-6907

Keywords:

AGB, RF, ANN, XGB, pastureland ecosystems

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.

Abstract
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