Author:

Keywords:

least-squares regression, vegetation, system, Science & Technology, Technology, Remote Sensing, Imaging Science & Photographic Technology, VEGETATION, SYSTEM, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering, Geological & Geomatics Engineering, 3706 Geophysics, 3709 Physical geography and environmental geoscience, 4013 Geomatic engineering

Abstract:

This paper illustrates a pilot study designed to examine the spectral response of soils due to salt variations. The aim of the study includes determining whether salt-affected soils can be discriminated based on their spectral characteristics, by establishing a relationship between soil properties and soil spectra and by testing if variations in the spectra of salt-affected soil samples are statistically significant. To answer the research questions, a laboratory experiment was designed to simulate salt transport to a column of soil in order to provide direct measurements of soil spectra and soil properties when salt concentration in a soil sample changes. The measured spectra were examined by the application of spectral matching techniques to quantify the variations and ascertain a relationship that supports the spectral identification of saline soils. The Ward's grouping method was conducted as an exploratory tool to statistically create homogeneous classes among data, which were obtained from the application of the spectral matching techniques to salt affected soil spectra. A nonparametric statistical test (Mann-Whitney U-test) was used to determine whether the differences between the classes are statistically significant. The results of spectral matching techniques showed differences in absorption strength, absolute reflectance and spectral angle in the near and short wave infrared regions. The results also showed significant correlations between soil electrical conductivity (EC) and spectral similarity measures, indicating that similarity between the samples' spectra decreases as the salt concentration in the soil increases. The generated clusters indicate two classes at the highest level, which were subdivided at the next level and further subdivided into multiple subclasses as the dissimilarity decreased. The spectral data were grouped into classes and were used to test the null hypothesis by applying the Mann-Whitney U-test. The results indicate a significance level of alpha< /0.02 between salinity classes and alpha< /0.05 per waveband, meaning variations between the classes are higher than within each class.