Abstract:
Mangrove ecosystems are important coastal habitats that contribute significantly to biomass carbon
accumulation and climate change mitigation through the organic storage in vegetation and soils.
Accurate estimation of these carbon stocks is essential for ecosystem monitoring, blue carbon
assessment, and sustainable management. However, conventional in-situ measurements are financially
demanding, logistically challenging, and scale poorly across vast ecosystems. Integrating remote
sensing with machine learning offers a viable, scalable alternative. This study investigates the
integration of Sentinel-1 radar and Sentinel-2 optical data for predicting mangrove biomass and carbon
stocks in Côte d’Ivoire. Despite their potential, a research gap exists in combining these data sources.
A dataset comprising 300 observations from the Fresco and Sassandra regions was utilized, including
field-measured above-ground biomass, below-ground biomass, total biomass, soil carbon stock, and
total carbon stock, together with Sentinel-2 optical bands (B2, B3, B4, and B8) and Sentinel-1 radar
backscatter variables (VV and VH). Data preprocessing involved normality assessment, logarithmic
transformation, and multicollinearity analysis using Variance Inflation Factor (VIF). Normalized
Difference Vegetation Index (NDVI) was derived and retained as a key predictor. Two modeling
approaches, chosen for their ability to handle non-linear relationships and uncertainty, were evaluated:
a Random Forest model with 10-fold cross-validation and a Bayesian Generalized Linear Model (GLM)
with Leave-One-Out Cross-Validation (LOO-CV). The Random Forest model achieved superior
predictive performance, with an RMSE of 0.885, MAE of 0.686, and R² of 0.283, reflecting signal
saturation in dense canopies and tidal background noise across landscapes. The Bayesian GLM yielded
an RMSE=0.924, MAE=0.695, and Bayesian R²=0.227. Variable importance analysis identified NDVI
as the strongest predictor, followed by radar backscatter variables VH and VV. Similar trends were
observed for carbon stock estimation, highlighting the value of integrating optical and radar
information. The findings demonstrate that combining Sentinel-1 and Sentinel-2 data provides a
scalable framework for operational monitoring, restoration planning, and blue carbon management.
