Advancing Mangrove Classification and Biomass Estimation in the Colombian Pacific Through Google AlphaEarth Foundations and Machine Learning

Mangrove ecosystems are critical for climate change mitigation; however, monitoring these environments in high-precipitation regions, such as the Colombian Pacific coast, is often hindered by persistent cloud cover and complex terrain. This study addresses these challenges by implementing the novel Google AlphaEarth Foundations (AEF) technology, leveraging 64-dimensional embeddings integrated with Digital Elevation Models (DEM) and Slope data. For classification, a Random Forest (RF) algorithm was deployed using a subset of only 7 embedding dimensions alongside topographical variables. The model estimated a mangrove extent of 209,262 ha, compared to a reference baseline of 137,732 ha. This discrepancy is hypothesized to stem from the model’s ability to map changes in the mangrove forest due to anthropogenic and natural factors and species migrating upstream, areas frequently overlooked in traditional inventories. The classification performance, evaluated on a spatially independent hold-out validation set, yielded an Overall Accuracy of 0.9844 and a Kappa Index of 0.9747. Regarding biomass estimation, the RF algorithm utilized 4 embedding dimensions plus DEM and Slope to achieve a Coefficient of Determination (R2) of 0.5844, a Root Mean Square Error (RMSE) of 18.99 Mg/ha, and a Mean Absolute Error (MAE) of 14.88 Mg/ha within a range of 100–300 Mg/ha. These metrics represent a notable advancement, successfully mitigating the physical signal saturation that typically constrains traditional single-sensor remote sensing models at high biomass thresholds. Significant advantages of this methodology include the complete elimination of cloud interference and a drastic reduction in processing time. These findings demonstrate that the synergy between foundational models and machine learning provides a robust, scalable, and efficient framework for managing blue carbon stocks in critical tropical regions.