Rapid prediction of soil mineralogy using imaging spectroscopy

Abstract

Hyperspectral images provide rich spectral and spatially continuous information that can be used for soil mineralogy discrimination. This paper proposes a method to evaluate the feasibility of Hyperion image in the rapid prediction of soil mineralogy. Four areas in Egypt were chosen for the current study. Preprocessing of the Hyperion data was done before applying the atmospheric correction. The minimum noise fraction transformation was used to segregate noise in the data. Various techniques were applied to the studied areas in which mixture tune matched filtering gave good results in a prediction of the end-members. Then, it employed to predict soil minerals in each cell using a spectral unmixing method. Illite, chlorite, calcite, dolomite, kaolinite, smectite, quartz, hematite, goethite, vermiculite, palygorskite and some feldspar were identified. In addition, sand and limestone, calcite and dolomite, and sand surface from similarly bright clouds can be distinguished easily based on the proposed method. The soil minerals obtained from X-ray diffraction analysis of the soil samples are in conformity with spectrally dominant mineralogy from Hyperion data. Different minerals can be identified using this method without any knowledge of field spectra or any a priori field data, thus configuring a “true” remote sensing method.