TY  - EJOU
AU  - Moradpour, Shohreh 
AU  - Zhao, Shuai 
AU  - Entezari, Mojgan 
AU  - Ayoubi, Shamsollah 
AU  - Mousavi, Seyed Roohollah 

TI  - Predicting Soil Carbon Pools in Central Iran Using Random Forest: Drivers and Uncertainty Analysis
T2  - Revue Internationale de Géomatique

PY  - 2025
VL  - 34
IS  - 1
SN  - 2116-7060

AB  - 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.
KW  - Hilly regions; topographic attributes; soil survey; organic matter; carbonates; random forest

DO  - 10.32604/rig.2025.069538