ABSTRACT: The study of soils, including their physical and chemical properties, is essential for agricultural management. Soil quality must be maintained to ensure sustainable production of food and conservation of natural resources. In this context, soil mapping is important to provide spatial information, which can be performed using remote sensing (RS) techniques. Modeling through use of satellite data is uncertain regarding the amplitude of replicability of the models. The aim of this study was to develop a quantification model for soil texture based on reflectance information from a continuum of bare soils, obtained by overlapping multi-temporal satellite images, and apply this model to an unknown region to evaluate its applicability. Spectral data were extracted from two Landsat TM 7 satellite images containing only bare soil, representing two distinct regions in Brazil (Area 1 and Area 2). The spectral data (obtained from six bands) and laboratory data (particle size from the 0.00-0.20 m layer) of Area 1 were modeled and extrapolated to Area 2. The bare soil images differentiated textural classes as sandy, sandy loam, clayey loam, clayey, and very clayey soil. The coefficients of determination between the determined and estimated values were higher than 0.5 and errors lower than 13 % for Area 1 and 30 % for Area 2, indicating applicability of the model to unknown areas.

ABSTRACT Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soil-landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area.

ABSTRACT It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.

RESUMOObjetivou-se neste trabalho caracterizar diferentes solos por espectrorradiometria de reflectância ao longo de uma topossequência na região de Piracicaba, SP. Amostras de solo foram coletadas e analisadas em campo, em laboratório de análises químicas e por sensores Vis-NIR (400-2500 nm). Alterações nos solos da topossequência foram identificáveis nas informações espectrais. Constituintes dos solos, tais como, matéria orgânica, mineralogia, formas de óxidos de ferro e granulometria foram determinantes nas variações das feições de absorção e intensidades de reflectância. Cada perfil mostrou características espectrais diferenciadoras entre horizontes, relacionadas à intensidade, feições de absorção e morfologia da curva. A avaliação morfológica não pode ser avaliada pelo sensor, sendo uma de suas limitações. Existe relação entre grau de intemperismo (índices ki, relação silte/argila e mineralogia) e dados espectrais. Isso foi observado nos solos originados de basalto, onde houve aumento do ferro extraído pelo ditionito (cristalino e amorfo) na sequência Nitossolo Vermelho Latossólico (NVL) em direção ao Cambissolo (C) e, aumento do ferro amorfo nesta mesma sequência. Na avaliação da topossequência completa observou-se a sequência de absorção centrada em 500 e 850 nm decrescente do Nitossolo Vermelho Latossólico em direção ao Chernossolo, ou seja, na sequência de decréscimo dos teores de ferro cristalino (hematita e goethita) e aumento de ferro amorfo, corroborado pelo aumento dos valores do índice ki. Houve relação entre os dados espectrais, o índice ki e a posição do solo na paisagem. Esses resultados mostram que a espectrorradiometria é uma ferramenta promissora para auxiliar o levantamento de solos. Entretanto, há necessidade do suporte à implantação de bibliotecas de dados espectrais de solos com acesso irrestrito aos usuários.

ABSTRACTThe aim of this work was to characterise by reflectance spectroradiometry different soils along a toposequence in Piracicaba, in the state of São Paulo, Brazil. Soil samples were collected and analysed in the field, in a laboratory for chemical analysis and by Vis-NIR sensors (400-2500 nm). Changes in the soils of the toposequence could be identified in the spectral information. Such soil constituents as organic matter, mineralogy, types of iron oxide and particle size determined variations in absorption features and reflectance intensities. Each profile displayed distinctive spectral characteristics between horizons, related to intensity, absorption features and curve morphology. The sensor could not be used to carry out the morphological evaluation, this being one of its limitations. There is a relationship between the degree of weathering (KI indices, silt to clay ratio and mineralogy) and the spectral data. This was seen in the basalt-based soils, where there was an increase in the iron extracted by dithionite (crystalline and amorphous) for the sequence, Latosolic Red Nitosol (NVL) to Cambisol (C), and an increase in amorphous iron for the same sequence. Evaluation of the complete toposequence showed that the absorption sequence centred on 500 and 850 nm decreased from the Latosolic Red Nitosol towards the Cambisol, i.e. following the decrease in crystalline iron content (hematite and goethite) and the increase in amorphous iron. This was substantiated by increases in KI index values. There was a relationship between the spectral data, KI index and landscape position of the soil. These results show that spectroradiometry is a promising tool to assist in soil surveys. However there is a need to support the implementation of libraries of soil spectral data, with unrestricted user access.

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.