Assessment of important soil properties related to Chinese Soil Taxonomy based on vis-NIR reflectance spectroscopy

As a rapid, inexpensive and accurate analysis technique, vis-NIR spectra has shown great advantages for determining a wide variety of soil properties, such as soil organic matter content, mineral composition, water content, particle size and color. Thus, this technique is becoming increasingly popular in soil science. We aim to assess the applicability of using vis-NIR spectra to estimate eighteen different soil properties that are important for Chinese Soil Taxonomy (CST). In this study, vis-NIR reflectance spectra were measured under laboratory conditions. First, partial least-squares regression (PLSR) was used to predict the following soil properties related to soil classification: coarse crumb, sand, silt, and clay contents, bulk density (BD), pH (H2O), pH (KCl), soil organic matter (SOM), total nitrogen (TN), total potassium (TIC), and total phosphorus (TP) contents, cation exchange capacity (CEC), free iron (Fe2O3), soluble salts (salt), available phosphorus (AP), exchangeable aluminum (ExAl), aluminum saturation (AS) and base saturation (BS). Then, the important bands for modeling these soil properties were selected based on the selectivity ratio (SR). Finally, the spectral chromophores of the soil and the correlations of soil properties were analyzed. The results showed that (1) the prediction accuracy based on the PLSR algorithm was good for pH, SOM, TN, Fe2O3, salt, AS and BS (RPD > 2.0, R-2 between 0.70 and 0.90). For sand, silt, clay, BD, TP, TK, CEC, AP and ExAl, the PLSR model could achieve acceptable estimates (1.4 < RPD < 2.0, R-2 between 0.56 and 0.72), while for coarse crumb, the PLSR model was unable to make reliable predictions (RPD < 1.4, R2 below 0.50). (2) As chromophore properties, SOM, TN, Fe2O3, clay and salt are and can be predicted by spectroscopy. Besides, BD, pH, TK, TP, CEC, AP, ExAl, AS and BS have significant correlations with chromophore properties and can also be predicted by vis-NIR spectroscopy. Therefore, except for coarse crumb, the soil properties important to CST can be quantitatively predicted by PLSR based on vis-NIR reflectance spectroscopy. This study is significant to CST, and it provides a fast and efficient method for soil classification.