Monitoring grasslands presents significant challenges due to temporal and spatial dynamics in their vegetation. This is particularly pronounced in wet grasslands, where moisture dynamics also impact vegetation patterns. Recent advancements in data acquisition and analysis via Unmanned Aerial Vehicles (UAVs) have shown potential for a more comprehensive understanding of vegetation dynamics. However, current UAV-based methods focus predominantly on structural and spectral data analysis. This often overlooks the horizontal heterogeneity within vegetation. This study addresses this gap by integrating texture analysis, alongside structural and spectral data, to enhance aboveground biomass (AGB) estimation. The research was conducted in a heterogeneous wet grassland in eastern Germany under three different cutting frequencies. Regular UAV flights were carried out to obtain RGB (Red, Green, and Blue) and multispectral images, analyzed alongside ground-reference data from 108 plots, to evaluate canopy height and biomass. We tested the performance of Random Forest and Partial Least Squares Regression models for AGB estimation considering different combinations of features including canopy height model vegetation indices and texture analysis. The results demonstrate that texture analysis when combined with traditional spectral and structural data, enhances predictive accuracy, yielding the best R² values of up to 0.84 for AGB and reducing the relative root mean square errors to 26.6 %. The results underline the potential of combining UAV-based features in AGB estimation of heterogeneous grassland ecosystems offering a path forward for more effective ecological monitoring and sustainable grassland management.