An Improved Vis-NIR Estimation Model of Soil Organic Matter Through the Artificial Samples Enhanced Calibration Set

A suitable calibration sample set is extremely important to acquire an accurate spectral-based model for estimating soil organic matter (SOM). However, an unrepresentative calibration sample set was frequently collected due to the inappropriate samplings pattern caused by problematic transportation logistics and complex geographic conditions, which resulted in fairly poor generalization and low accuracy of the spectroscopic model. Thus, we hypothesized that a soil sample dataset equivalent to natural soil samples could be prepared under controlled laboratory conditions, and increase the accuracy of spectroscopic estimation of SOM content by use of a coverage assessment method that added laboratory-simulated near-natural samples to the natural samples set in order to enhance the representative sample size and variability of the calibration set. The results showed that the near-natural samples enhanced (NSE) calibration set contained 42 natural soil samples and 28 near-natural soil samples. This set exhibited sufficient coverage and better information integrity within estimators space than the initial calibration set that included 43 natural soil samples. Random forest model based on the NSE calibration set (R-2 = 0.90; RPIQ = 4.17) more accurately estimated SOM content than the spectral-based model built with the initial calibration set (R-2 = 0.73; RPIQ = 2.32); the SOM chemical compositions (e.g., lipids, polysaccharides, and lignin) and their relative abundance from the laboratory-simulated near-natural soil samples were basically consistent with those of natural soil samples. The inclusion of near-natural soil samples in the calibration set improved the SOM spectral-based estimation model, and was observed to be a practical method. Our results provided a calibration set enhancement strategy that effectively supports spectroscopic estimation model of SOM contents in the case of pattern-biased field samplings at the local scale.