Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Abstract

Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R2 = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R2 = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques.