Learning from multimodal and multisensor earth observation dataset for improving estimates of mangrove soil organic carbon in Vietnam

Quantifying mangrove soil organic carbon (SOC) is key to better understanding the global carbon cycle, a critical phenomenon in reducing greenhouse gas emissions. However, it is challenging to have a large sample size in soil carbon measurements and analysis due to the high costs associated with them. In the current research, we propose a novel hybridized artificial intelligence model based on the categorical boosting regression (CBR) and the particle swarm optimization (PSO) algorithm for feature selection, namely, the CBR-PSO model for estimating mangrove SOC. We integrated multisensor optical (Sentinel-2) and synthetic aperture radar (Sentinel-1 and ALOS-2 PALSAR-2) remote sensing data to construct and verify the proposed model, drawing upon a survey in 85 soil cores at 100 cm depth in the Red River Delta, Vietnam. The CBR-PSO model estimated the mangrove SOC ranging from 44.74 to 91.92 Mg ha⁻¹ (average = 68.76 Mg ha⁻¹) with satisfactory accuracy (coefficient of determination (R ²) = 0.809 and root-mean-square error (RMSE) = 9.30 Mg ha⁻¹). We also compared the proposed model’s capability with four machine learning techniques, i.e. support vector regression (SVR), random forest regression (RFR), extreme gradient boosting regression (XGBR), and XGBR-PSO models. We show that multimodal and multisensor earth observation dataset combined with the CBR-PSO model can significantly improve the estimates of mangrove SOC. Our findings contribute novel and advanced machine learning approaches for robustness of SOC estimation using open-source software. Our novel framework, which is automated, fast, and reliable, developed in this study can be easily applicable to other mangrove ecosystems across the world, thus providing insights for a voluntary blue carbon offset marketplace for sustainable mangrove conservation.