Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra.

Visible and near-infrared (Vis-NIR) spectroscopy is a promising alternative to replace soil physicochemical Analysis to quickly and effectively determine the content of soil organic matter (SOM). However, choosing appropriate pre-processing methods and effective data mining techniques is the essential step in Vis-NIR to improve the quality of spectral data and the accuracy of the model prediction. In this study, nine spectral pre-processing methods and optimal band combination algorithms were introduced to process the spectra and select sensitive spectral parameters. The purpose of this study is to determine the effective pre-processing method and explore the prediction potential of the optimal band combination algorithm. Two hundred thirty-three soil samples were gathered from northwestern Xinjiang, China, and the soil properties and reflectance spectra were measured in the laboratory. The spectra were subjected to nine pre-processing methods, e.g., Savitzky-Golay (SG) smoothing, discrete wavelet transformation (DWT), First (FD) and second (SD) derivatives, multiplicative scatter correction (MSC), standard normal variate and detrend (SNV-DT), continuum removal (CR), correction by the maximum reflectance (CMR) and pseudo-absorbance values and detrend (Abs-DT). The results indicate, the SG proved to be the most effective pre-processing method for SOM in saline soil. The Abs-DT, FD, SD, SNV-DT, MSC, CR, DWT, and CMR led to degrading the prediction performance. Furthermore, the use of SG before further processing can improve the prediction effect, although it is not obvious. The optimal band combination algorithm can derive spectral parameters that have a good correlation with SOM content. Prediction accuracy (RPIQ was 3.058 and 3.045 in independent and cross-validation respectively) and model complexity (latent variables were both 4) from spectral parameter combination were both better than that from full-spectrum data. In summary, the combination of SG and the optimal band combination algorithm can improve the prediction accuracy of SOM in saline soil.