Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky-Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc(2) of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv(2) of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

Automated summary

Using VIS-NIR technology and multiple regression analysis, a highly accurate and stable prediction model for soil organic matter content was developed in the forest of the Greater Khingan Mountains. By combining various preprocessing algorithms and characteristic variable selection methods, significant improvements in model performance and prediction efficiency were achieved. The results showed that the combination of S-G smoothing with SNV or MSC preprocessing strategies had the best performance in enhancing the model accuracy.