Enhancing CO₂ Sequestration Modeling in Mangrove Forests Through Random Forest Hyperparameter Optimization and Remote Sensing Band Analysis

Coastal wetlands play a crucial role in the global carbon cycle, yet accurately estimating CO2 emissions from these ecosystems remains challenging. This paper presents a novel approach to estimate CO2 sequestration potential in coastal wetlands and mangrove forests, integrating remote sensing band analysis with Random Forest (RF) modeling and hyperparameter tuning through Grid Search, and Bayesian optimization. Utilizing multi-temporal Landsat 8 and Sentinel-2 imagery, we developed a linear regression (LR) model to predict CO2 concentrations, emphasizing the role of mangroves in mitigating greenhouse gases. Our RF model achieved an RMSE of 5.10e-12 and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 1.0, demonstrating exceptional accuracy. In contrast, Bayesian optimization yielded an RMSE of 64.49 with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.997, while Grid Search produced an RMSE of 63.29 and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.997. Additionally, our analysis revealed significant clustering of high-emission areas, primarily linked to degraded mangrove forests and aquaculture zones. These findings underscore the critical role of mangrove ecosystems in carbon sequestration and provide valuable insights for more effective climate change mitigation strategies in coastal wetlands.