Characterizing regional soil mineral composition using spectroscopy and geostatistics
| Autor: | Vera Leatitia Mulder, S. de Bruin, Michael E. Schaepman, Raymond F. Kokaly, Jorg Weyermann |
|---|---|
| Přispěvatelé: | University of Zurich, Mulder, V L |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
010504 meteorology & atmospheric sciences
variables Soil Science Mineralogy attributes Geostatistics 01 natural sciences Advanced Spaceborne Thermal Emission and Reflection Radiometer Laboratory of Geo-information Science and Remote Sensing Kriging vegetation Linear regression spatial prediction Kaolinite Laboratorium voor Geo-informatiekunde en Remote Sensing variograms Computers in Earth Sciences 910 Geography & travel 1111 Soil Science 1907 Geology 0105 earth and related environmental sciences Remote sensing model carbon 1903 Computers in Earth Sciences Geology area 04 agricultural and veterinary sciences PE&RC usgs tetracorder 10122 Institute of Geography Digital soil mapping 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Spatial variability regression Mica |
| Zdroj: | Remote Sensing of Environment 139 (2013) Remote Sensing of Environment, 139, 415-429 |
| ISSN: | 0034-4257 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect