Applicability of the thermal infrared spectral region for the prediction of soil properties across semi-arid agricultural landscapes

32Citations
Citations of this article
65Readers
Mendeley users who have this article in their library.

Abstract

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 μm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (μFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4-2.5 μm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils' chemical and physical composition. The study revealed the potential of the TIR (for clay: R2 = 0.93, RMSEP = 0.66% and for sand: R2 = 0.93, RMSEP = 0.82%) and its combination with the solar reflective region (for organic carbon: R2 = 0.95, RMSEP = 0.04%) for retrieving soil properties in typical soils of semi-arid regions. The models' drivers confirmed the opto-physical base of most of the soils' constituents (clay minerals, silicates, iron oxides), and emphasizes the TIR's advantage for soils with compositions dominated by quartz and kaolinite. © 2012 by the authors.

Cite

CITATION STYLE

APA

Eisele, A., Lau, I., Hewson, R., Carter, D., Wheaton, B., Ong, C., … Kaufmann, H. (2012). Applicability of the thermal infrared spectral region for the prediction of soil properties across semi-arid agricultural landscapes. Remote Sensing, 4(11), 3265–3286. https://doi.org/10.3390/rs4113265

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free