High-Resolution Mapping of Soil Organic Carbon Stocks Using Machine and Deep Learning Approaches Across Mediterranean Land Uses
Résumé
Soils play crucial role as reservoir of organic carbon, reflecting the quality and fertility of terrestrial ecosystems. Consequently, understanding the spatial distribution of soil organic carbon (SOC) stocks and the factors that influence these distributions is imperative for ensuring environmental sustainability and achieving carbon neutrality. This study compares four algorithms namely, Random Forest (RF), Gradient Boosting Machine (GBM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), which use 29 environmental covariates and 442 soil samples from various land use types to predict and map SOC stocks at a depth of 0–30 cm in the Aix-Marseille-Provence (AMP) Metropolis, France. The results revealed that forests presented the highest SOC content (57 g·kg⁻1) and stock (7.5 kg·m⁻2), while vineyards displayed the lowest values (SOC content: 8.9 g·kg⁻1; stock: 3.4 kg·m⁻2). Urban areas exhibited significant SOC levels, influenced by human activity, with an average content of 45.6 g·kg⁻1 and a stock of 6.2 kg·m⁻2. The Shapley values method revealed that precipitation, elevation, land cover, vegetation index, and temperature were the major factors contributing to the prediction of the SOC stock. The SOC stock prediction algorithms revealed that the RF outperformed the other algorithms (R2 = 0.83, RMSE = 1.41 kg·m⁻2, MAE = 0.97 kg·m⁻2). The area of applicability (AOA) function demonstrated that the RF model was reliable, as almost all the predicted areas fell within the AOA. These results could lead to the development of guidelines for facilitating the sustainable management of carbon sequestration in various land use types within the AMP Metropolis and other Mediterranean regions.