Assessment of aboveground biomass using integrated multi-source remote sensing and field data in forests of the Upper Blue Nile basin Northwestern Ethiopia

The increasing availability of remotely sensed data has significantly advanced forest biomass estimation by improving prediction accuracy across local and global scales. Accurate estimation of forest aboveground biomass density (AGBD) requires establishing strong correlations between field-measured biomass and remotely sensed variables. In this study, forest canopy height was modeled from Planet-NICFI, Sentinel-2, Sentinel-1, and GEDI L2A product datasets by employing a Random Forest model on the Google Earth Engine. Forest AGBD was further predicted from GEDI L2A, ALOS PALSAR-2, and Sentinel-2 data, both independently and through multi-sensor integration, using field-derived AGBD as reference data in the Random Forest model. Thus, integrating GEDI L2A product RH98 observations with Planet-NICFI imagery using a Random Forest model showed higher modeling performance with (R² = 0.89 and RMSE value 1.91 m) and developed a 5 m spatial-resolution forest canopy-height product for the year 2023. Likewise, the integration of GEDI L2A, ALOS PALSAR-2, and Sentinel-2 variables yielded excellent AGBD prediction performance (R² = 0.972, RMSE = 18.27 Mg/ha). Subsequently, an AGBD product at 10 m spatial resolution was developed by integrating multisource remote-sensing and topographic variables for the year 2023. A comparison between remotely sensed and field-measured AGBD showed similar spatial distribution patterns, with AGBD predicted by the GEDI L2A-derived forest canopy height product providing enhanced detail and accuracy in dense, high-canopy forests. These findings demonstrate that integrating LiDAR, RADAR, and optical datasets effectively alleviates the limitations and saturation effects inherent in single-sensor approaches. Overall, this study demonstrates the performance of multi-sensor data fusion for improving AGBD estimation accuracy and provides valuable suggestions for evidence-based forest policy, climate change mitigation, and sustainable forest ecosystem management.