Prediction of soil carbon and nitrogen components from excitation emission matrix using machine learning

Abstract

Estimating greenhouse gas emissions from soil and their underlying factor components is crucial for addressing global warming. This study aimed to develop predictive models for soil total carbon (TC), total nitrogen (TN), water-soluble total organic carbon (WS-TOC), water-soluble total nitrogen (WS-TN), and CO₂ emissions using excitation-emission matrices (EEM) of soil water extracts as input. The models were evaluated using root mean square error of cross-validation (RMSE_CV), coefficient of determination (R²_CV), and ratio of performance to deviation of cross-validation (RPD_CV) on test data. Comparison between convolutional neural networks (CNN) and partial least squares regression (PLSR) demonstrated that CNN achieved superior performance, enabling highly accurate estimations for TC (R²_CV = 0.95), TN (R²_CV= 0.93), WS-TOC (R²_CV = 0.86), and WS-TN (R²_CV = 0.91). These results suggest that fluorescence spectroscopy combined with deep learning has the potential to accurately estimate carbon and nitrogen- related components in soil.