@Article{rig.2025.069538,
AUTHOR = {Shohreh Moradpour, Shuai Zhao, Mojgan Entezari, Shamsollah Ayoubi, Seyed Roohollah Mousavi},
TITLE = {Predicting Soil Carbon Pools in Central Iran Using Random Forest: Drivers and Uncertainty Analysis},
JOURNAL = {Revue Internationale de Géomatique},
VOLUME = {34},
YEAR = {2025},
NUMBER = {1},
PAGES = {809--829},
URL = {http://www.techscience.com/RIG/v34n1/64398},
ISSN = {2116-7060},
ABSTRACT = {Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, and geomorphic units were most influential. Climatic factors showed minimal impact in the studied semi-arid watershed. The RF model achieved moderate prediction accuracy (SOC: R<sup>2</sup> = 0.43 ± 0.13, nRMSE = 0.28; SIC: R<sup>2</sup> = 0.47 ± 0.11, nRMSE = 0.37). Via and PDP analyses enhanced model interpretability by clarifying environmental influences on SOC/SIC spatial distribution.},
DOI = {10.32604/rig.2025.069538}
}