Estimation of soil organic carbon (SOC) is very useful for accurate monitoring of carbon sequestration. However, there are still significant gaps in the knowledge of SOC reserves in many parts of the world, including western Iran. To partially fill the gap, 865 soil samples were used with 101 auxiliary variables and 5 machine learning (ML) algorithms to digitally map SOC for the plough layer (0–30 cm) at a 90-m resolution in Kurdistan province. Results indicated that the most important auxiliary variables were rainfall (27.09%), valley depth (26.66%), terrain surface texture (23.42%), air temperature (20.18%), channel network base level (16.61%) and terrain vector roughness (14.47%). Results also showed that Random Forests (RF) performed best in predicting the spatial distribution of SOC (RMSE = 0.35% and R² = 0.60), compared to the other ML algorithms (i.e. Cubist: CU, k-Nearest Neighbor: kNN, Extreme Gradient Boosting: XGBoost and Support Vector Machines: SVM). Furthermore, results estimated the total SOC stocks (SOCS) for the whole study area (~15,208 Tg) and amounts under different land uses. These were bareland (~6 Tg), orchard (~356 Tg), irrigated farming (~782 Tg), forest (~1773 Tg), grassland (~5991 Tg) and dry farming (~6297 Tg). As expected, the SOCS were highest in forest soils (652 g m⁻²) and lowest in bareland (437 g m⁻²). This result suggests that the conversion of native land (e.g. Forest) to cultivated land (e.g. Irrigated farming) could lead to significant loss of SOCS and appropriate management of land use could increase SOCS.

估算土壤有机碳（SOC）对于准确监测碳固存非常有用。但是，在世界许多地区，包括伊朗西部，SOC储备的知识仍然存在很大差距。为了部分填补空白，在库尔德斯坦省以90 m的分辨率，使用865个土壤样本和101个辅助变量和5种机器学习（ML）算法以数字方式绘制了耕层（0–30 cm）的SOC。结果表明，最重要的辅助变量是降雨（27.09％），谷深（26.66％），地形表面纹理（23.42％），气温（20.18％），渠道网络基础水平（16.61％）和地形矢量粗糙度（ 14.47％）。结果还表明，随机森林（RF）在预测SOC的空间分布方面表现最佳（RMSE = 0.35％和R ² = 0.60），与其他ML算法（即Cubist：CU，k最近邻：k NN，极限梯度提升：XGBoost和支持向量机：SVM）相比。此外，结果估计了整个研究区域（〜15,208 Tg）的总SOC储量（SOCS）和不同土地利用下的量。它们分别是：荒地（〜6 Tg），果园（〜356 Tg），灌溉农业（〜782 Tg），森林（〜1773 Tg），草地（〜5991 Tg）和旱作（〜6297 Tg）。如预期的那样，SOCS在森林土壤中最高（652 g m ^-2），而在荒地中最低（437 g m ^-2）。该结果表明，将原始土地（例如森林）转换为耕地（例如灌溉耕作）可能导致SOCS大量流失，对土地利用的适当管理可能会增加SOCS。