Spatial prediction of soil organic carbon using machine learning techniques in western Iran

• Published in: Geoderma Regional Vol. 21; p. e00260
• Main Authors: Mahmoudzadeh, Hamid, Matinfar, Hamid Reza, Taghizadeh-Mehrjardi, Ruhollah, Kerry, Ruth
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020
• Subjects: air temperature; Aridisols; carbon sequestration; dryland farming; forest soils; forests; grasslands; Iran; irrigated farming; Land use; landscapes; Machine learning algorithms; orchards; prediction; rain; Random forests; roughness; soil organic carbon; Soil organic carbon stocks; soil sampling; Spatial prediction; support vector machines; texture; Iran; Spatial prediction; Machine learning algorithms; Aridisols; Soil organic carbon stocks; Random forests; Land use
• ISSN: 2352-0094; 2352-0094
• DOI: 10.1016/j.geodrs.2020.e00260

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. •Spatial distribution of SOC in western Iran at a fine resolution was predicted.•Different machine learning algorithms were compared.•SOC stocks was assessed in different land uses.