Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon

Abstract

Spectral techniques such as visible-to-near-infrared (VIS–NIR) and mid-infrared (MIR) spectroscopies have been regarded as effective alternatives to laboratory-based methods for determining soil organic carbon (SOC). Research to explore the potential of the fusion of VIS–NIR and MIR absorbance for improving SOC prediction is needed, since each individual spectral range may not contain sufficient information to yield reasonable estimation accuracy. Here, we investigated two data fusion strategies that differed in input data, including direct concatenation of full-spectral absorbance and concatenation of selected predictors by optimal band combination (OBC) algorithm. Specifically, continuous wavelet transform (CWT) was adopted to optimize the spectral data before and after data fusion. Prediction models for SOC were developed using partial least squares regression. Results demonstrated that estimations for SOC using MIR absorbance (i.e., validation R2 = 0.45–0.64) generally outperformed those using VIS–NIR (i.e., validation R2 = 0.20–0.44). Compared to the raw absorbance counterparts, CWT decomposing could improve the prediction accuracy for SOC, for both the individual absorbance and the fusion of VIS–NIR and MIR absorbance. Among all the models investigated, the combinational use of VIS–NIR and MIR using OBC fusion at CWT scale of 1 yielded the optimal prediction, providing the highest validation R2 of 0.66. This model with 10 selected spectral parameters as input is of small total data volume, large processing speed and efficiency, confirming the potential of OBC in fusing both types of spectral data. In summary, CWT decomposing and OBC strategy are powerful algorithms in analyzing the spectral data, and allow the VIS–NIR and MIR spectral fusion models to improve the SOC estimation.