Variations in Reflectance of Tropical Soils: Spectral-Chemical Composition Relationships from AVIRIS data

• Published in: Remote sensing of environment Vol. 75; no. 2; pp. 245 - 255
• Main Authors: Galvão, Lênio Soares, Pizarro, Marco Antônio, Epiphanio, José Carlos Neves
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01-02-2001; Elsevier Science
• Subjects: Applied geophysics; Earth sciences; Earth, ocean, space; Exact sciences and technology; Internal geophysics; Soils; Surficial geology; Brazil; South America; mixing; titanium; vegetation; aluminum; principal components analysis; spatial distribution; clay; soils; clastic rocks; reflectance; opaque minerals; remote sensing; cartography; sedimentary rocks; sand; airborne methods; silicon; correlation; regression; organic materials; prediction; iron; soil erosion; chemical composition
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/S0034-4257(00)00170-X

The relationships between Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) surface reflectance values and constituents (total iron, organic matter, TiO
2, Al
2O
3, and SiO
2) of samples representative of three important soil types from central Brazil [Terra Roxa Estruturada (S
TE), Latossolo Vermelho-Escuro (S
LE), and Areia Quartzosa (S
AQ)] were analyzed. End member spectra for green vegetation (GV), nonphotosynthetic vegetation (NPV), water (W), and the three soil types were selected by inspecting scatter plots derived from the principal components analysis (PCA) of 140 AVIRIS bands. They were then used to compose a six end member unmixing model to characterize the spectral reflectance variations associated with the different scene components, the spatial distribution of the soil types, and the effects of spectral mixing on the spectral-chemical composition relationships. Finally, regression equations fitted to soil constituents and their highly correlated spectral bands were used to produce maps showing the chemical variability in the scene for areas dominated by the presence of exposed soils, as indicated by the results from the unmixing model. The results showed a very good agreement between the spatial variability of the soil types and of the soil constituents. The largest squared correlation results were obtained for Fe
2O
3, TiO
2, and Al
2O
3, but the relationships were affected in the transition from the red to the near-infrared interval by the presence of nonsoil residues (e.g., senescent vegetation or litter) over the soil surfaces. In comparison with the light and loamy sand S
AQ, the dark-red clay S
TE and S
LE presented higher contents of Fe
2O
3, Al
2O
3, and TiO
2, and consequently lower overall reflectance in the scene, because of the presence of greater amounts of opaque minerals. The prediction of these constituents from remote sensing data and their close association with the spatial distribution of the different soil types demonstrate the importance of the present investigation for soil mapping and soil erosion studies.
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