Strategies for predicting soil organic matter in the field using the Chinese Vis-NIR soil spectral library

The large-scale soil spectral library (SSL) can provide abundant information for predicting soil properties at a local scale, especially in places lacking data. However, since all the existing large-scale SSLs only contain dry spectra recorded under laboratory conditions, the challenge remains in using SSL for predicting soil information using in situ soil spectra. Previous studies have focused on the methods of transforming in situ spectra to dry spectra when using SSLs, while few studies have compared which strategies are optimal in predicting soil properties. To determine the optimal strategies for predicting soil organic matter (SOM) from an area not located in the archived Chinese Soil Spectral Library (CSSL), we investigated the prediction accuracy of memory-based learning (MBL) using spectra transformed by external parameter orthogonalization (EPO) on the CSSL (MBL_EPO) and on the CSSL spiked with subset samples selected by the conditioned Latin hypercube (cLH) algorithm (MBL_EPO_spiking) and using the data from CSSL spiked directedly by the subset in situ samples (MBL_wet_spiking). We also evaluated the prediction accuracy of the in situ and dry spectra using the selected subset and the partial least squares regression (PLSR) model directly. The results showed that the mean squared Euclidean distance (msd) calculated from spectra was an optimal indicator for selecting the representative samples for both the laboratory and in situ conditions. When only 20 samples with both in situ and dry spectra are available to predict SOM, MBL_EPO_spiking is suggested; otherwise, MBL_wet_spiking with the spiking of in situ spectra determined by the smallest msd is optimal. Our findings pave the way for efficient SOM prediction in situ by integrating a large-scale SSL.