Characterizing regional soil mineral composition using spectroscopy and geostatistics

• Published in: Remote sensing of environment 2013-12, Vol.139, p.415-429
• Main Authors: Mulder, V.L., de Bruin, S., Weyermann, J., Kokaly, R.F., Schaepman, M.E.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; area; ASTER; attributes; Biological and medical sciences; carbon; Digital soil mapping; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fixed Rank Kriging; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Mineral identification; regression; spatial prediction; Spectral feature comparison; Spectroscopy; Teledetection and vegetation maps; usgs tetracorder; variables; variograms; vegetation
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2013.08.018

This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD) was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We tested whether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was done with Fixed Rank Kriging (FRK) to represent the medium and long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2=0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2=0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale. •Fixed Rank Kriging was used for extracting scale-dependent variability from RS data.•Regional soil mineralogy was characterized using medium and long-scaled RS data.•Model accuracy improved using multi-scale soil–landscape relationships.•Combining spectroscopy, RS and geostatistics is efficient for large-scale mapping.