Estimation of aboveground biomass in Tajikistan based on upscaling extrapolation of UAV and Sentinel-2 multi-source data synergy

Grasslands constitute the largest terrestrial ecosystem, currently sequestering significant amounts of atmospheric carbon and playing a critical role in climate change mitigation and the global carbon cycle. Tajikistan is a key representative of Central Asian, effective and accurate grassland Above-ground biomass (AGB) monitoring in Tajikistan is crucial for sustainable management. However, the relevant research remains markedly limited. Here we develop a dynamic sampling scale-up method for AGB estimation by integrating multi-source data from Sentinel-2 MSI, Unmanned Aerial Vehicle (UAVs), and ground observations, which enabled efficient and accurate AGB estimation across Tajikistan. Specifically, our analyses combine UAV and Sentinel-2 multispectral imagery with field-measured data to construct and optimize eight models for AGB estimation at different spatial scales, and apply the dynamic sampling scale-up method to enhance estimation accuracy. We find that: (1) At the UAV scale, the Extra Trees model achieves the highest accuracy (R2 = 0.88, RMSE = 52.72 g ·m−2), whereas at the Sentinel-2 scale, the Support Vector Machine (SVM) model performs best (R2 = 0.80, RMSE = 158.43 g ·m−2); (2) Texture features are the most crucial features for grassland AGB estimation; (3) The scale-up method improves the accuracy of Sentinel-2-derived AGB estimations, enabling more detailed spatial representation of AGB distribution. Our results demonstrate that coordinated multi-source monitoring elucidates the environmental controls on grassland AGB and provides a robust framework for conservation and sustainable management of grassland ecosystems under current and future climate scenarios.

Abstract licence: CC BY-NC-ND

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