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This study aims to identify the mechanisms driving soil organic carbon distribution in arid regions.

February 9, 2026Open Access

Detecting Drivers and Predicting Spatial Distribution of Soil Organic Carbon in an Arid Region Using Machine Learning

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This study aims to identify the mechanisms driving soil organic carbon distribution in arid regions.
Applied four machine learning models to predict soil organic carbon content.
Used SHAP analysis to evaluate contributions of key environmental variables.
Produced high-resolution spatial maps of soil organic carbon.
Gradient Boosting model outperformed others with R2 = 0.675 and RMSE = 1.304 g kg−1.
NDVI, DEM, sand, clay, mean temperature, and ERVI influenced soil organic carbon variation.
Higher SOC was found in mountainous zones and vegetated valleys; lower in flat, arid plains.

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Machine learning models predict spatial distribution of soil organic carbon in arid regions, indicating key environmental influences.

Chen et al. (2026) studied this question.

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