Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China

Abstract

Soil salinization, which is one of the most important land degradation problems in arid and semiarid regions, has a significant impact on ecological equilibrium. Hyperspectral remote sensing, with a large number of measured wavelength bands and a high resolution, has gradually become a popular technology to investigate soil salinization. In this study, a model based on field-derived spectra was developed for soil salinity and the quantitative relationships between the soil spectrum and vegetation spectrum with the soil salt content (SSC) and soil electrical conductivity (EC). A field study was performed in Minqin County, China. A genetic algorithm (GA), partial least squares regression (PLS), and back-propagation neural network (BPNN) were used for modeling. The results showed that GA has a relatively strong ability for band selection. After the selection, the predictive ability of the GA-PLS model was better than the PLS model based on the full spectra. The BPNN model built by selected bands (GA-BP) was superior to the GA-PLS linear model. The models built using the soil spectrum after band selection have a high predictive ability. The R2 and ratio of prediction to deviation (RPD) for SSC were 0.68 and 1.76 for GA-PLS and 0.72 and 1.89 for GA-BP, respectively. There were no significant correlational relationships between the normalized difference vegetation index and SSC or EC. The GA-BP model fitted using the vegetation spectrum was superior to a single vegetation index model, whereas the predictive ability of SSC (0.56 for R2 and 1.47 for RPD) was not high due to influences such as plant species differences and vegetation coverage. Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved.