Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs. However, remote sensing data and its combination with soil properties were reliably used to predict PSPs (R (2) =0.41 for MBC by RF model, R (2)=0.49 for PBC by Cu model, R (2)=0.37 for SPR by Cu model, and R (2)=0.38 for SBC by RF model). The lowest RMSE values were obtained for MBC by RF model, PBC by SVM model, SPR by Cubist model and SBC by RF model. The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area. The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity (CEC) and clay content, and among the remote sensing indices B5/B7, Midindex, Coloration index, Saturation index, and OSAVI were the most imperative factors for predicting PSPs. Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.

Automated summary

In this study, different machine learning models (Cu, RF, SVM, GPR) were used to predict soil phosphorous sorption parameters (PSPs). The results showed that using topographic attributes alone was not sufficient for accurate prediction of PSPs, but combining remote sensing data with soil properties reliably predicted PSPs. The RF model had the lowest RMSE values for MBC, the SVM model for PBC, the Cubist model for SPR, and the RF model for SBC. The study concluded that remote sensing data was an easily obtainable dataset that could reliably predict PSPs in the study area.