Mapping the Salt Content in Soil Profiles using Vis-NIR Hyperspectral Imaging

Recently, visible and near-infrared (Vis-NIR) hyperspectral imaging has shown great potential in fine mapping of soil properties in laboratory. Whether it could be used to predict soil salt content (SSC) in the soil profile under field conditions still remained to be determined. In this study, hyperspectral images were acquired in situ from a soil profile with a Vis-NIR imaging spectrometer, and the optimum SSC prediction model was built to determine SSC of each pixel, and the fine SSC distribution maps were generated. The observed soil profile was located at an experimental station in Dongtai City, Jiangsu Province, China. Hyperspectral images with a spectral range of 397 to 1018 nm were obtained from 21 to 25 May 2015; a total of 140 soil samples were collected. Five spectral preprocessing methods, Daubechies wavelet (Db), LOG(10)(1/Db), Savitzky-Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) were applied, and partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models were developed. Results showed that the LS-SVM model predicted the SSC more accurately than the PLSR model, and the highest prediction accuracy was obtained with LOG(10)(1/Db) preprocessed spectra with R-p(2), RMSEp, RPIQ, and RPD values of 0.87, 0.58 g kg(-1), 2.60 and 2.77, respectively. Based on the optimum prediction model, the fine distribution of SSC in soil profiles over 5 d were successfully obtained. This study indicated hyperspectral imaging is an efficient and nondestructive method for mapping SSC distribution in soil profiles and characterizing the vertical transportation of soil salt under field conditions with moderate soil moisture range.