Comparative analysis of global forest above-ground biomass using temporal remote sensing products (Conference Presentation)

Le résumé fourni par la source

Accurate estimation of global above-ground biomass (AGB) is critical for carbon cycle modeling, yet inconsistencies among remote sensing products hinder reliable assessments. This study evaluates three global time-series AGB datasets (Xu, Liu, and Wigneron Biomass) using high-resolution reference data. Results show Xu Biomass outperforms others in tropical and temperate regions due to high-resolution data fusion and disturbance modeling, but struggles in alpine ecosystems. Liu Biomass exhibits systematic overestimation linked to coarse-resolution vegetation optical depth (VOD), while Wigneron Biomass faces sensor penetration limitations in low-biomass areas. Temporal analyses reveal divergent mutation points and trends driven by algorithm-specific biases. Our findings highlight the necessity of ecosystem-specific calibration and multi-source data fusion to reduce uncertainties. This work provides actionable insights for optimizing AGB product selection in carbon policy frameworks

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