Applicability of the Thermal Infrared Spectral Region for the Prediction of 
Soil Properties Across Semi-Arid Agricultural Landscapes

Eisele, Andreas; Lau, I.; Hewson, R.; Carter, D.; Wheaton, B.; Ong, C.; Cudahy, J.; Chabrillat, Sabine; Kaufmann, Hermann

doi:10.3390/rs4113265

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Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes

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/persons/resource/eisele

Eisele, Andreas
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Lau, I.
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Hewson, R.
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Carter, D.
External Organizations;

Wheaton, B.
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Ong, C.
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Cudahy, J.
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Chabrillat, Sabine
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/charly

Kaufmann, Hermann
1.4 Remote Sensing, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Zitation

Eisele, A., Lau, I., Hewson, R., Carter, D., Wheaton, B., Ong, C., Cudahy, J., Chabrillat, S., 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

https://gfzpublic.gfz-potsdam.de/pubman/item/item_245514

Zusammenfassung

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 Remote Sens. 2012, 4 3266 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.