ABSTRACT. Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

RESUMO O intemperismo do material de origem é o processo inicial que garantirá ao solo maior ou menor fertilidade, tornando o estudo desse processo fundamental para entender a dinâmica do solo. Grande parte dos estudos de intemperismo é baseada em índices que calculam a taxa de dissolução dos feldspatos (CIA, CIW e PIA, por exemplo) muitas vezes não refletindo a complexidade do intemperismo ocorrendo no perfil de solo. Outras metodologias- como balanço geoquímico, diferença elementar (DE) e índice W- têm sido desenhadas para melhorar esses estudos. Avaliar se essas metodologias serão mais eficientes que os índices CIA, CIW e PIA para apontar as variações intempéricas em perfis recentes desenvolvidos de basalto é o objetivo desse trabalho. Foram utilizados dados de óxidos principais para calcular o balanço geoquímico, DE, CIA, CIW, PIA e W e analisar a intensidade do intemperismo em dois perfis originados de basalto no Estado de São Paulo. Foram fatorados também os índices Ki e Kr e a relação Fe o/Fed e analisada a mineralogia dos horizontes dos solos e da rocha de origem. O índice W e a DE mostraram maior variação intempérica que CIA, CIW e PIA. O balanço geoquímico, a mineralogia e as razões Ki e Kr, Feo /Fed foram eficientes em explicar as variações que ocorreram entre os horizontes. A junção da DE com o balanço geoquímico trouxe novas informações para a análise do intemperismo que a observação isolada de CIA, CIW e PIA.

ABSTRACT Weathering of the parent material is the initial process that plays an essential role in soil fertility. The study of this process has become crucial to better understand soil dynamics. Most studies on weathering are based on indexes that calculate the feldspar dissolution rate (for example, CIA, CIW, and PIA) which not always reflect the complexity in the soil profile. Other methodologies have been designed to improve these approaches, such as geochemical balance, difference of elemental contents (DE), and the W index. This work aim to assess the efficiency of these methods to identify variations in recent soil profiles developed from diabase as compared to the CIA, CIW, and PIA indexes. Data on the main oxides were used to calculate the geochemical balance, DE, CIA, CIW, PIA, and W indexes to analyze weathering intensity in two profiles originated from diabase in the São Paulo State, Brazil. The Ki and Kr indexes and Feo /Fed ratio were also factored and we analyzed the mineralogy of soil horizons and the source rock. The W index and DE presented greater weathering variation than the CIA, CIW, and PIA in both profiles. The geochemical balance, mineralogy, and the Ki, Kr and Feo /Fed ratios explained the variations between the horizons. The DE and geochemical balance together generated new information for the analysis of the weathering process than the isolated indexes.

ABSTRACT Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos).

ABSTRACT Applying the upcoming technologies in agriculture has been a major economic, environmental and social challenge for scientists and farmers. In order to overcome such challenge, this study evaluated the advantages and limitations of using geostatistics and machine learning for soil mapping in agricultural practices and soil surveys. The study occurred in Tocantins State, Brazil, and consisted into seven areas with a total extension of 17.24 km2, 222 meters regular gridded resulting in one-point sampling per 0.0493 km2 of five randomly sampled cores within a 1 m circle radius. It was collected 332 georeferenced soil samples at 0-20 cm depth using an auger and then, soil laboratory analyses performed. Afterward, liming rate maps were originated from the predicted soil attributes clay, cation exchange capacity and base saturation comparing four methods: ordinary kriging, random forest, cubist, support vector machine and the best model results of each soil attribute. Evaluating the methods, the Pearson’s index presented strong results for soil attributes predicted by random forest and ordinary kriging. Machine learning methods can be successfully applied for soil mapping in agricultural practices and soil surveys using less soil samples rather than geostatistical framework.

ABSTRACT The success of soil prediction by VIS-NIR-SWIR spectroscopy has led to considerable investment in large soil spectral libraries. The aims of this study were 1) to develop a soil VIS-NIR-SWIR spectroscopy approach using legacy soil samples to improve spectral soil information in a regional scale; (2) to compare six spectral preprocessing techniques; and (3) to compare the performance of linear and non-linear multivariate models for prediction of sand and clay content. A total of 1,534 legacy soil samples, stored by Epagri, were collected from agricultural areas in 2009 on a regional scale, covering 260 municipalities of Santa Catarina. Six spectral preprocessing techniques were applied and compared with reflectance spectra (control treatment) in the development of sand and clay prediction models. Five multivariate regression models, Support Vector Machines, Gaussian Process Regression, Cubist, Random Forest, and Partial Least Square Regression were compared. The scatter-corrective preprocessing groups produced similar or better performance than spectral-derivatives. In addition, preprocessing spectra prior to regression analysis does not improve sand prediction, since reflectance spectra achieved the best performance using Cubist, SVM, and PLS models. In general, clay content presented better prediction accuracy than sand content. The best multivariate model to predict sand and clay content from soil VIS-NIR-SWIR spectra was Cubist. The best Cubist performance was achieved combined with reflectance spectra (R2 = 0.73; root mean square error = 10.60 %; ratio of the performance to the interquartile range = 2.36) and MSC (R2 = 0.83; root mean square error = 7.29 %; ratio of the performance to the interquartile range = 3.70) for sand and clay content, respectively. Considering the mean RMSE values of the validation set, the predictive ability of the multivariate models decreased in the following order: Cubist>PLS>RF>GPR>SVM for both properties. The predictive ability of VIS-NIR-SWIR reflectance spectroscopy achieved in this study for sand and clay content using legacy soil data and heterogeneous samples confirmed the potential of the spectroscopy approach.

ABSTRACT: Traditional method of soil survey is expensive, slow, and must be carried out by experienced researchers. Thus, advances in soil observation technologies and the need to obtain information quickly by modern techniques have intensified the use of proximal sensing. This study characterized surface reflectance spectra (A horizon) and related them to traditional soil classification, based on morphological, physical, and chemical properties of representative pedogenetic profiles, developed in two toposequences of the Distrito Federal, Brazil. In the toposequences, 15 soil profiles were selected for a complete morphological description and sampling for laboratory analyses. Soil-landscape relationships were established, and profiles were classified to the fourth level of the Brazilian Soil Classification System (SiBCS). Classes of similar soils were grouped based on their surface spectra, resulting in spectral curves of 10 representative soils in the studied area. The Morphological Interpretation of Reflectance Spectrum (MIRS) and second derivative of the Kubelka-Munk (KM) function were applied to the soil spectra. The clustered soils were similar, mainly in terms of color, textural class, and organic matter content. Groups based on soil physical and chemical properties and on surface and subsurface colors were similar to those determined by surface reflectance. The identification of soil-landscape relationships was fundamental to understand the genesis and distribution of the soils, which had similar chemical and physical properties to their parent materials. The analysis of clusters based on soil surface reflectance proved efficient in determining groups of soil classes with similar properties. Surface reflectance data were related to the soil surface and subsurface properties determined by traditional soil sample analyses, since the two approaches formed similar groups. The simultaneous interaction of soil properties was assessed by MIRS analysis, while the second derivative of the KM function adequately quantified the mineralogy of the spectra.

ABSTRACT Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models. Soil samples were collected in Iconha, Espírito Santo State, Brazil, in the Ribeirão Inhaúma basin. A total of 184 samples were collected from 92 sites at two depths (0.00-0.10 and 0.10-0.30 m). Physical, chemical, and spectral analyses were performed according to routine soil laboratory methods. Random selection was made of 70 % of total samples for training and 30 % for validation of the models. The coefficient of determination (R2) and root mean square error (RMSE) were calculated in order to assess model performance. The standardized indexes of prediction error RPD and RPIQ were also calculated. For clay and OC, the best R2 was found in the MIR spectrum, at 0.69 and 0.65, respectively, and for P, it was 0.57 in Vis-NIR. The MSC (Multiplicative Scatter Correction), CR (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR - Partial Least Squares Regression (OC and P) and SVM - Support Vector Machine (clay) gave the best predictions and are therefore recommended for modeling these properties in the study area. The models identified in this study can be used to discriminate soils according to a critical test value for clay, OC, and P.

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 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.

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.

Os componentes do solo como matéria orgânica, teor e forma dos óxidos de ferro, umidade e textura, influenciam na interação deste com a energia eletromagnética, sendo assim o conhecimento da energia refletida por diferentes classes de solos pode auxiliar em suas discriminações. O objetivo do presente trabalho foi avaliar a possibilidade de diferenciar classes de solos ao longo de dois transectos, por meio do comportamento espectral de campo de diferentes classes, utilizando para tanto a análise visual e estatística multivariada. Nos transectos foram demarcados pontos equidistantes em 100 m, de onde coletaram-se amostras de solo georreferenciadas da camada superficial, na profundidade de 0 a 0,20 m. Na área em estudo, foram descritas 10 classes de solos, por meio de análises de laboratório das amostras superficiais e descrição morfológica de perfis, sendo cada solo classificado de acordo com o Sistema Brasileiro de Classificação de Solos. Para separação das unidades de solos e estabelecimento de seus limites na paisagem utilizaram-se três métodos: a) tradicional, avaliando-se a correlação solo-paisagem; b) avaliação qualitativa das curvas espectrais e c) análise multivariada para discriminação das classes de solos. A comparação entre os métodos possibilitou avaliar o uso da espectrorradiometria na diferenciação de classes de solos, sendo que o método estatístico separou maior número de classes. Foi possível delimitar os limites das unidades de solos da área de estudo bem como as diferenças de textura, cor, ferro total e matéria orgânica entre as unidades, por meio da variação nas respostas espectrais das amostras de solos avaliadas.

Soil components, such as organic matter, the levels and form of iron oxides, moisture and texture, influence the interaction of the soil with electromagnetic energy, so that knowledge of the energy reflected by different classes of soil is therefore of help in their discrimination. The aim of this study was to evaluate the possibility of differentiating classes of soils along two transects, by means of the field spectral behaviour of different classes, using both visual and multivariate statistical analysis. Equidistant points were marked along the transects, 100 m apart, where geo-referenced soil samples were collected from the surface layer at a depth of 0 to 0.20 m. In the area under study, ten classes of soil were described by means of laboratory analysis of surface samples and a morphological description of the profiles, with each soil being classified according to the Brazilian System of Soil Classification. For separating soil units and establishing their boundaries in the landscape three methods were used: a) traditional, evaluating the correlation between soil and landscape; b) qualitative assessment of the spectral curves; c) multivariate analysis for discrimination of the classes of soil. Comparison of these methods made possible the use of spectroradiometry in differentiating the classes of soil, with the statistical method separating a larger number of classes. It was possible to delimit the boundaries of the soil units in the area under study, as well as differences in texture, colour, total iron and organic matter between units, by varying the spectral responses of the soil samples being tested.

A região de Areiópolis, Estado de São Paulo, é uma das principais produtoras de cana-deaçúcar do mundo e o manejo químico é a base para o sistema, tornando sua produção viável. Assim, métodos propostos pela agricultura de precisão podem ser avaliados como alternativa à proteção ambiental e a maior produtividade. O objetivo deste trabalho foi comparar os sistemas de produção, Agricultura de Precisão (AP) e a Agricultura Tradicional (AT) e destacar suas distinções, tais como as diferenças na grade de amostragem, a variação da produção; stand e custos. Dois experimentos foram instalados e o método de aplicação de fertilizantes e corretivos foi conduzido pela média da fertilidade geral (AT), com a aplicação localizada em lotes de 16-ha. O método de AP indicou maior produtividade, tanto para matéria verde quanto para açúcar no lote 4,0-ha; com a diminuição do tamanho dos lotes em AP, os custos das análises de solo aumentaram, principalmente para o potássio e o calcário; o grid mais adequado em termos de produtividade e relação custo benefício foi o de uma amostra a cada 4,0-ha; em termos gerais, o custo final foi maior na AP. Os resultados fundamentam a decisão sobre qual o melhor sistema para a gestão de solos tropicais.

The region of Areiópolis in São Paulo State is one of the major sugarcane producers in the world, and chemical management is the basis of the system making its production viable. Thus, the proposed methods for precision agriculture can be evaluated as an alternative for environment protection and can aid the search for greater productivity at the same time. The main objective of the present work was to compare the precision agriculture (PA) and traditional agriculture (TA) management systems and to highlight their distinctions, such as differences in grid sampling, production variation, plant failure and costs. Two experiments were set up, and the soil fertilizers were applied by corrective application methods to 16-ha lots using the average general fertility rate (GFR). The PA method had the highest productivity volume for conversion of green matter to sugar in the 4.0-ha plots. As the size of the PA plots decreased, the costs of soil analyses increased with potassium and lime analyses being the most expensive. The PA plots had more suitable grid sampling in terms of productivity, and the cost/benefit ratio was 4.0-ha. In general, the final cost was higher in the PA system compared to the TA system. The present results provide information to help select the better system between these techniques to manage tropical soils.

Uma vez que pedólogos diferentes produzirão, em uma mesma área, mapas de solos distintos, é importante avaliar as divergências existentes entre profissionais e técnicas de mapeamento, dentre elas o Mapeamento Digital de Solos, que vem sendo muito abordada atualmente. Para tanto, compararam-se quatro mapas detalhados de solos (escala 1:10.000) de uma fazenda de 182 ha cultivados com cana-de-açúcar, localizada no município de Rafard, SP, com grande variação do meio físico. Tais mapas foram elaborados independentemente por quatro pedólogos e confrontados com outro obtido com o apoio de uma técnica de mapeamento digital de solos. Aos pedólogos, foram fornecidas mesmas informações; cada um foi levado ao campo o número de vezes que julgou necessário para examinar os solos (paisagem e perfis), delinear as unidades de mapeamento (UM) e indicar os locais onde desejava examinar o perfil do solo e, ou, amostrá-lo para análises laboratoriais. Para o MDS, foram utilizados dados espectrais de uma imagem do sensor TM do satélite Landsat 5 e seis atributos de terreno derivados de carta planialtimétrica. Essas informações foram resumidas por análise de componentes principais e utilizadas para geração dos delineamentos em um mapa de grupos, com auxílio da análise de grupamentos Fuzzy K-médias. Posteriormente, em cada delineamento, foram feitas observações de campo para identificar os solos componentes das UMs, de acordo com o Sistema Brasileiro de Classificação de Solos (SiBCS). Para comparações entre os mapas convencionais e entre esses e o mapa digital de solos (MDS), efetuaram-se cruzamentos entre os mapas dois a dois, gerando matrizes de confusão, apresentadas na forma de mapas. A análise das correspondências espaciais, para cada nível categórico do SiBCS, permitiu verificar que a concordância entre a correspondência na identificação das UMs nos mapas decresce para os níveis categóricos inferiores e que o executor exerce grande influência nos delineamentos e na identificação das UMs do mapa de solos. A correspondência espacial média entre mapas convencionais e entre esses e o MDS foram semelhantes. Portanto, o método utilizado para obter esse MDS proporcionou resultados similares aos obtidos com os mapas convencionais, com as vantagens de quantificar informações sobre as características da paisagem de cada solo, úteis para extrapolar resultados para futuros levantamentos que forem efetuados em áreas com solos similares.

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

A obtenção de informações sobre solo de modo sistematizado é necessária com a crescente demanda no sistema agrícola e ambiental. Este trabalho objetiva testar a técnica do modelo de análise de mistura espectral com múltiplos membros finais (MESMA) para mapear atributos do solo com base em uma imagem ASTER. Para tanto, foram utilizadas 184 amostras georeferenciadas de solo da região de Rafard. Estes pontos foram sobrepostos a imagem de satélite para a coleta dos dados espectrais. Na sequência, as informações de laboratório e da imagem foram combinadas e procedeu-se o agrupamento das amostras em classes para os atributos textura, matéria orgânica, saturação por bases, CTC e ferro total. Após classificadas, geraram-se curvas médias para cada classe de atributo. As informações espectrais foram utilizadas como membros finais para a geração dos mapas através do MESMA. Mapas dos mesmos atributos também foram obtidos através de análise geoestatística. A partir dos dois mapas gerados, realizouse uma análise de tabulação cruzada para verificar a eficiência do MESMA no mapeamento de atributos do solo. Observou-se elevada similaridade entre os mapas de textura, matéria orgânica, CTC e ferro total. Conclui-se que a metodologia mostra-se eficiente no mapeamento dos atributos do solo estudados.

Systematic, physically based acquisition of information regarding soils is required to meet increasing demand in agricultural and environmental systems. The objective of this work is to evaluate the use of multiple endmember spectral mixture analysis (MESMA) for mapping soil attributes within ASTER imagery. A total of 184 georeferenced soil samples were collected from Rafard, São Paulo State, Brazil. These points were overlain on the satellite image to collect spectral data. The laboratory and image information were then arranged and prepared by clustering samples into classes based on the following soil attributes: texture, organic matter, base saturation (V%), CEC and total iron. Following this classification, mean spectral curves were generated for each attribute class. Spectral curves were used as endmembers for the generation of maps using MESMA. Maps of the same attributes were also generated using geostatistical analyses. Based on the two generated maps, a crosstabulation was used to evaluate the accuracy of MESMA for mapping soil attributes. Agreement was high for maps of the texture, organic matter, CEC and total iron. We conclude that the methodology used in this work was efficient for mapping soil attributes.

O objetivo deste trabalho foi desenvolver e testar um método para a determinação da classe de solo e sua separabilidade na paisagem dos solos presentes em uma área de estudo localizada no estado de São Paulo. Um conjunto de equações discriminantes foi obtido utilizando-se o sistema SAS que permitiu estabelecer a classe de solo na área de estudo. Foram analisadas 16 classes de solos as quais foram estabelecidas equações discriminantes compostas pelas bandas 1, 2, 3, 4, 5, e 7 do sensor TM5/ Landsat. As leituras espectrais foram realizadas em 185 pontos da área de estudo, donde se extraiu a reflectância da imagem. Classes de solos podem ser individualizadas por meio de análise discriminante utilizando-se informações sobre seu comportamento espectral obtida pela metodologia apresentada. A análise discriminante apresentou índices de acerto acima de 40% dentro da classe de solo avaliada, para 14 das 16 classes de solos. Utilizando-se as dez classes com maior número de áreas mínimas cartografadas o acerto, dentro a classe, foi maior que 50% para sete classes de solos, com acerto global estabelecido em 52%. Quando se agrupou as classes de solos em função do seu material de origem, o acerto passou para 70%.

The aim of this study was to develop and test a method to determine and discriminate soil classes in the state of São Paulo, Brazil, based on spectral data obtained via Landsat satellite imagery. Satellite reflectance images were extracted from 185 spectral reading points, and discriminant equations were obtained to establish each soil class within the studied area. Sixteen soil classes were analyzed, and discriminant equations that comprised TM5/Landsat sensor bands 1, 2, 3, 4, 5, and 7 were established. The results showed that this methodology could effectively identify individual soil classes using discriminant analyses of the spectral data obtained from the surface. Success rates of > 40% were achieved for 14 of the 16 evaluated soil classes when applying the satellite image data. When the 10 soil classes containing the largest number of minimum cartographic areas were used, the hit rate increased to > 50%, for seven soil classes with a global hit rate of 52%. When the soil classes were grouped based on their parent materials, the hit rate increased to 70%. Thus, we concluded that the spectral method for soil classification was efficient.