Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon

Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity, nutrient availability and potentially represents a measurement of the sink for greenhouse gas abatement. Improving our knowledge on the spatial distribution of SOC is hence essential for sustainable nutrient management and carbon storage capacity. The objective of this study was to evaluate the performance of six tree-based machine-learning models when using environmental variables (i.e., remote sensing and terrain attributes - scenario 1), Fourier Transform Infrared Spectroscopy (FTIR) data (scenario 2) and combination of environmental variables and FTIR data (scenario 3) as predictors in prediction of SOC content. The models included Random Forest, Cubist, Conditional Inference Forest, Conditional Inference Trees, Extreme Gradient Boosting and Classification, Regression Trees. Furthermore, we explored if the Bat optimization algorithm can improve the prediction accuracy of the models. The study was conducted across a 7000 ha field in the Miandoab County, Northern Iran, with a total of 80 soil samples collected systematically in a regular grid (700 x 1000 m). According to Leave-One-Out Cross-Validation, the best prediction performance was achieved by the Cubist+Bat model when environmental variables and FTIR spectra (scenario 3) were used (Coefficient of determination = 0.73, Concordance Correlation Coefficient = 0.77, Root Mean Square Error = 0.36, Mean Absolute Error = 0.31, Median Absolute Error = 0.28). FTIR data had the highest influence on the prediction accuracy of SOC. Therefore, it can be concluded that the combination of environmental variables and FTIR data with Cubist+Bat model as a precise approach to monitor SOC in semi-arid soils of Iran. The final Digital Soil Map (DSM) of SOC revealed that improvements in prediction might be possible with the collection of more soil samples in areas where the land use and topography changed over short spatial scales.

Automated summary

The study evaluated six tree-based machine-learning models for predicting soil organic carbon content using environmental variables and FTIR data, with the Cubist+Bat model performing the best and FTIR data having the highest influence on prediction accuracy.