2016-12-23T18:37:48

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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 (R2 > 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 R2 = 0.72). The use of soil analysis methodology by satellite and/or ground remote sensing constitutes an alternative to traditional routine laboratory analysis.

In the study of physical, chemical, and mineralogical data related to the weathering of soils and the quantification of their properties, remote sensing constitutes an important technique that, in addition to conventional analyses, can contribute to soil survey. The objectives of this research were to characterize and differentiate soils developed from basaltic rocks that occur in the Paraná state, Brazil and to quantify soil properties based on their spectral reflectance. These observations were used to verify the relationship between the soils and reflectance with regard to weathering, organic matter (OM), and forms of Fe. From the least to the most weathered soil, we used a Typic Argiudoll (Reddish Brunizem), Rhodudalf (Terra Roxa Estruturada), and Rhodic Hapludox (Very Dark Red Latosol). The spectral reflectances between 400 and 2500 nm were obtained in the laboratory from soil samples collected at two depth increments, 0‐ to 20‐ and 40‐ to 60‐cm, using an Infra Red Intelligent Spectroradiometer (IRIS). Correlation, regression, and discriminant estimates were used in analyzing the soil and spectral data. Results of this study indicated that soils could be separated at the soil‐type level based on reflectance intensity in various absorption bands. Soil collected in the 40‐ to 60‐cm depth appeared to have higher reflectance intensities than those from the 0‐ to 20‐cm depth. Removal of OM from soil samples promoted higher reflectance intensity in the entire spectrum. Amorphous and crystalline Fe influenced reflectance differently. Weathering of basaltic soils was correlated with alterations in the reflectance intensities and absorption features of the spectral curves. Multivariate analysis demonstrated that this technique was efficient in the estimation of clay, silt, kaolinite, crystalline Fe, amorphous Fe, and Mg through the use of reflected energy of the soils.

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SUMMARYSoil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a