Modeling and estimating the spatial distribution of soil organic matter content in irrigated lands

Soil characteristics depend on its various physical, chemical, and biological parameters. Soil organic matter content (SOMC) is one of the main parameters of soil that affects all of these parameters. Therefore, this study was performed to estimate SOMC of irrigated lands in the Damavand Absard region of Tehran province using terrain attributes and standardized spectral reflectance (ZPC1) through two modeling approaches of random forest (RF) and artificial neural network (ANN). Soil sampling (0–30 cm) was performed at 60 points using random sampling method, and SOMC was measured in the laboratory. The results of SOMC modeling showed that the RF model predicted SOMC more accurately than the ANN model. Modeling SOMC using RF model showed that the R2, RMSE, and MAE values for training data were 0.81, 0.23%, and 0.22%, respectively. Furthermore, the R2, RMSE, and MAE values for test data were 0.78, 0.20%, and 0.21%, respectively. Modeling SOMC stocks by RF model showed that the efficient factors for estimating the SOC stocks were ZPC1, Slope, Normalized height, Relative Slope Position, elevation, Vertical Distance to Channel Network, Standardized height, and Slope length (LS) factor. According to the obtained results, the combination of terrain attributes and ZPC1 data can be used for managing and monitoring the SOMC in the irrigated lands of Iran with sufficient accuracy. Based on the results of this study, we conclude that the farmers can reference these maps to manage the SOMC in the Damavand Absard region due to high importance of this area for irrigated agriculture.