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Forest ecosystems play a vital role in balancing the global climate through functions such as regulating carbon emissions, carbon sequestration, and energy and water cycles. Aboveground biomass (AGB) is a critical component in forest management to understand better and predict the global carbon cycle. However, traditional methods used in AGB measurement involve time-consuming, costly, and labor-intensive processes. Sentinel-1 (active), Sentinel-2, and Landsat (passive) satellite imagery, which is freely accessible and offers global coverage with frequent updates, and recently developed remote sensing platforms such as Unmanned Aerial Vehicle (UAV) serve as a valuable data source for consistent and continuous monitoring of aboveground biomass. This research focuses on modeling the relationships between AGB and data obtained from various remote sensing sources, including Sentinel-1, Sentinel-2, Landsat 8, and UAV imagery, within pure Scots pine stands in northern Türkiye. The study employs multiple linear regression (MLR) and artificial neural networks (ANNs) to establish these relationships. AGB values for each sample plot were calculated using an allometric equation. Backscatter coefficients and band brightness values were extracted from Sentinel-1 imagery, while reflectance values and vegetation indices were generated from Sentinel-2, Landsat 8 OLI, and UAV imagery. Additionally, texture features were computed for varying window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, and 15 × 15) and orientations (0°, 45°, 90°, and 135°) based on data from Sentinel-2 and Landsat 8 OLI images for each sample plot. The relationships between remote sensing data and AGB were modeled using both MLR and ANN techniques. The findings revealed that the most accurate AGB estimation (R²=0.82; RMSE = 0.35 ton ha⁻¹) was achieved using the texture variables derived from the 9 × 9 window size of Sentinel-2 imagery via the ANNs modeling approach, outperforming other image sources and MLR analysis. [ABSTRACT FROM AUTHOR]