Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Abstract

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and shortwave infrared (SWIR) wavelengths (0.3-2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0-20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52-0.86 μm) and SWIR (AST5-9: 1.60-2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R 2 correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.

Abordagens semiquantitativa e quantitativa na avaliação da textura do solo por espectroscopia de reflectância bidirecional no VIS-NIR-SWIR

“…Isto ocorreu principalmente pela maior agregação dos solos mais argilosos, o que, provavelmente, causou maior absorção da energia eletromagnética incidente e, também, pela influência do comportamento espectral do quartzo, com forte albedo e ausência de feições de absorção no VIS-NIR-SWIR, e pelo aumento da absorção ocasionada por minerais opacos, principalmente em solos argilosos e muito argilosos, ao longo de toda a assinatura espectral (Bellinaso et al, 2010;Vicente & Souza Filho, 2011). Assim como observado por Demattê (2002), verificouse alteração dos ângulos das curvas espectrais, pois, os solos com maior teor de argila tenderam a ter aspecto plano e, à medida que aumentou o teor de areia, aumentou também a intensidade do infravermelho com ápice de reflectância a partir de 2.000 nm.…”

“…Também a resposta espectral do quartzo no VIS-NIR-SWIR, mineral abundante na fração areia, é caracterizada por forte albedo e ausência de feições de absorção, o que é geralmente observado no espectro de solos arenosos (Bellinaso et al, 2010). A presença de minerais opacos como magnetita e ilmenita, em solos derivados de material de origem rico em ferro, afetam as leituras espectrais, por apresentar albedo muito baixo e ausência de feições de absorção nesta região do espectro eletromagnético (Vicente & Souza Filho, 2011). Demattê & Garcia (1999) destacam o potencial da espectroscopia de reflectância do VIS-NIR-SWIR para o estudo do solo, inclusive quanto à predição da textura, em diferentes regiões do Brasil.…”

Séries temporais de NDVI do sensor SPOT Vegetation e algoritmo SAM aplicados ao mapeamento de cana‑de‑açúcar

“…Assim, sua utilização está associada ao mapeamento semiquantitativo de dados hiperespectrais (Schowengerdt, 1997;Vicente & Souza Filho, 2011). No caso de abordagens de larga escala com sensores de resolução moderada, esses critérios de conhecimento de cena e contiguidade espectral são difíceis de se obter, mas são eficientes na identificação e na classificação de alvos (Williams & Hunt Junior, 2002).…”

Application of high altitude and ground-based spectroradiometry to mapping hazardous low-pH material derived from the Sokolov open-pit mine

“…Although this method was designed primarily for HS image data analysis, it has frequently been used for mapping sub-pixel abundances using multispectral data (Dawelbait & Morari 2012;Pacheco & McNairn, 2010;Parente, Bishop, & Bell, 2009;Shanmugam, Ahn, & Sanjeevi, 2006;Vicente & de Souza Filho, 2011). The only condition is that the number of derived fractions (end members) is equal to or less than the number of bands.…”

“…Although ASTER data do not provide users with hundreds of narrow and contiguous spectral bands, the main advantage of HS remote sensing that allows quantitative analysis of surface components, these data still have the potential to identify diverse minerals and map relative abundances (Van Der Meer et al, 2012). The VNIR bands have been shown to be capable of mapping the relative abundances of iron oxides/ hydroxides (Vicente & de Souza Filho, 2011), and SWIR bands can be used to separate advanced mineral groups including argillic minerals (kaolinite, alunite, dickite), phyllic alteration minerals (sericite), and propylitic minerals (calcite, epidote, chlorite) (Mars & Rowan, 2010;Yamaguchi & Naito, 2003).…”