Abstract
Soil mineralogy is an important factor affecting chemical and physical processes in the soil. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the longwave infrared (LWIR) region. The current study presents a procedure for determining the soil mineralogy from the surface emissivity spectrum. Ground-based hyperspectral LWIR images of 90 Israeli soil samples were acquired with the Telops Hyper-Cam sensor, and the emissivity spectrum of each sample was calculated. Mineral-related emissivity features were identified and used to create indicants and indices to determine the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant minerals. The resultant mineral content was in good agreement with the mineralogy derived from chemical analyses.
Highlights
Soil is a complex material that is extremely variable in its physical and chemical composition
Recent studies have shown that the thermal infrared region, in both the mid wavelength infrared (3–5 μm) and the long wavelength infrared (LWIR, 8–12 μm) regions can provide quantitative information on soil properties, such as texture, carbon and nitrogen content, and pH, especially when large datasets are processed with statistical models (e.g., [7,8,9,10,11])
Soil mineralogy is an important factor in determining its properties, quality, and growth potential, and is extensively used as a diagnostic criterion in comprehensive soil classification
Summary
Soil is a complex material that is extremely variable in its physical and chemical composition. We presented a procedure that can be applied to hyperspectral LWIR images to calculate surface emissivity, an important variable for mineral mapping, and to identify the dominant minerals, quartz, feldspars, clay minerals, gypsum, and carbonates in rocks [12,13]. Hyperspectral remote sensing in the LWIR region, used with significant success for the mapping of the content of minerals on rock surfaces (e.g., [14,15]), has not been fully implemented for the complex and dynamic soil material, and its potential has not yet been fully exploited. The current study makes use of ground-based hyperspectral LWIR images, acquired with the Telops Hyper-Cam hyperspectral sensor [16], to calculate the emissivity spectra of soil samples, representing the soil’s chemical and physical properties, and to analyze them to identify quartz, clay minerals, carbonates, and their abundance, in each sample. The resultant mineral content was correlated to the chemical elements’ abundance and the mineral composition as obtained from chemical analyses
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