Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations

Análise discriminante dos solos por meio da resposta espectral no nível terrestre Soil discrimination analysis by spectral response in the ground level

O objetivo deste trabalho foi desenvolver e avaliar um método para discriminação das classes de solos a partir de suas respostas espectrais, utilizando-se um sensor em laboratório. Os dados espectrais foram utilizados no desenvolvimento de modelos estatísticos para discriminar as classes de solos de uma área no sudoeste do Estado de São Paulo. Equações discriminantes foram desenvolvidas para as 18 classes. A resposta espectral foi obtida em amostras da porção superficial e da porção subsuperficial dos solos da área de estudo, num total de 370 amostras. As amostras foram coletadas em 185 ha, com uma tradagem por ha. Os resultados demonstraram que as classes de solos podem ser individualizadas e distinguidas pela análise discriminante. A análise registrou índices de acerto acima de 80% de determinação da classe de solo avaliada. O acerto global foi de 90,71% quando se utilizaram todas as classes para a geração dos modelos, e 93,44% quando se utilizaram as dez classes com maior número de indivíduos. O teste estatístico simulado mostrou-se eficiente na análise discriminante, com taxa média de acerto acima de 91%, com erro global de 8,8%. A análise demonstrou redução na qualidade do modelo quando aplicado para um subconjunto de 20% das amostras, com erro global de 33,9%. O método auxilia na discriminação de classes de solos pela sua reflectância, devido às interações físicas com a energia eletromagnética.<br>The objective of this study was to develop and test a discrimination method for soil classes by their spectral response (SR), using a laboratory sensor. Spectral data were used to develop statistical model for discriminating soil classes in an area at the southwest of São Paulo State, Brazil. Discriminant equations were developed for 18 soil classes. The spectral data were obtained in superficial and subsuperficial soil samples in the study area, with a total of 370 samples. The samples were collected in 185 ha, with one borehole per ha. The results showed that soil classes can be separated and delimitated by discriminant analysis. The analysis presented a classification index higher than 80% for each soil class. The global classification index was 90.71%, when all soil classes were used to develop the model, and 93.44% when most individuals classes were used. The simulated statistical test was efficient in the discriminant analysis, presenting a classification index higher than 91%, with a global error of 8.8%. The analysis demonstrated a reduction of the model quality when applied for 20% sub-group of the samples with global error of 33.9%. The method helped in the soil classes discrimination by their spectral reflectance, based on their physical interaction with electromagnetic energy