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Soil Science Society of America Journal Abstract - Pedology

Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis


This article in SSSAJ

  1. Vol. 70 No. 2, p. 393-407
    Received: Nov 11, 2003

    * Corresponding author(s): jamdemat@carpa.ciagri.usp.br
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  1. Marcos Rafael Nannia and
  2. José Alexandre M. Demattê *b
  1. a Agronomy Dep., Maringá State Univ., 87020-900, Maringá, Paraná, Brazil
    b Soil Science and Plant Nutrition Dep., Univ. of São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, C.P. 9, 13418-900, Piracicaba, São Paulo, Brazil


Traditional soil analyses are expensive, time-consuming, and may also result in environmental pollutants. The objective of this study was to develop and evaluate a methodology to measure soil attributes using spectral reflectance (SR) as an alternative to traditional methods. Tropical Brazilian soils were sampled over a 196-ha area divided into grids. Samples (n = 184) were obtained from the 0- to 20- and 80- to 100-cm depths and georeferenced. The laboratory SR data were obtained using a Spectroradiometer (400–2 500 nm). Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Particle-size distribution and chemical analysis (organic matter [OM], cation-exchange capacity [CEC], total SiO2, Fe2O3, TiO2, sum of cations, cation, and Al saturation) were performed in the laboratory. Statistical analysis and multiple regression equations for soil attribute predictions using radiometric data were developed. Laboratory data used 22 bands and 13 “Reflectance Inflexion Differences, RID” from different wavelength intervals of the optical spectrum. However, the satellite data used only the reflectance of the 1, 2, 3, 4, 5, and 7 TM-Landsat bands. Multiple regression equations were derived from surface and subsurface soil layers. Estimations of some tropical soil attributes were possible using laboratory spectral analysis. Laboratory SR yielded high correlations with traditional laboratory analyses (R 2 > 0.79) for the soil attributes such as clay, sand, TiO2, and Fe2O3 Satellite spectral data correlated well with most of the soil attributes such as clay, Fe2O3, and TiO2 (reaching R 2 = 0.72). The use of soil analysis methodology by satellite and/or ground remote sensing constitutes an alternative to traditional routine laboratory analysis.

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