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ABSTRACT The application of rock dust (RD) in agricultural soils has shown the capacity to improve soil conditions for plant growth. However, the successful use of RD requires understanding its effects on weathering in soil surfaces and its possible products. In this study, we present the results of an exploratory test in which a RD had its weathering intensified in a soil column by temperature (45 °C) and intense leaching (1,339.2 mm in 24 weeks), in the absence of plants. There was a significant increase in amorphous phases of iron (Fe) and particularly of aluminum (Al) to the amount of 0.13 g kg –1 for Fe and 0.16 g kg –1 for Al in the 0-0.05 m layer of the column and 0.22 g kg –1 for Al in the 0.05-0.10 m layer. These values account for 6 % of the total Fe, and 27 % of the total Al added as RD in the column. Spectral features in the visible-near infrared-short wave infrared (VIS-NIR-SWIR) and mid-infrared (MIR) range, as well as X-ray diffraction (XRD) scans, were inconclusive because of the small signal-noise ratio of the newly precipitated phases. Selective chemical methods targeted to specific soil pools (precipitated, adsorbed, and leached) were more sensitive to capture the new amorphous phases resulting from the RD dissolution. This study contributes to understanding the effects of RD on the availability of ions and the possibility of amorphous oxides precipitating in the soil. In addition, it helps identify other effects associated with using RD, such as changes in the pH, cation exchange capacity (CEC), and nutrient availability.

ABSTRACT: It is known that floriculture is very important for the world economic scenario. Nevertheless, the vase life of cut flowers is determined by a short time span. This study evaluated the spectral behavior during leaf degradation of lisianthus (Eustoma grandiflorum) flower stems in the postharvest, in different preservative solutions, and estimating its leaf pigments by hyperspectral data. Lisianthus floral stems were subjected to preservative solutions with different concentrations of sucrose, glucose, phytohormones and deionized water. Leaves from these stems were collected every 4 days for further hyperspectral analysis. Spectra was determined in laboratory with a sensor collecting at the range of 350-2500 nm. In order to analyze the use of the spectra to detect the performance of the preservative solutions, statistical tests were used at each wavelength. Pigment prediction was assessed by the CAR/CLF ratio (Ratio between Carotenoids and Chlorophyll) from the spectral curves, using PLSR and RRMSE. The glucose-based solutions registered positive effects on the preservation of floral stems, especially at the dose of 180 g/L. The solutions based on sucrose and phytohormones registered similarities in the spectral curves among the doses, demonstrating equality in leaf preservation. The discriminant analyses demonstrated there was statistical difference in the spectral responses in the doses for each solution used. The CAR/CLF ratio had reasonable coefficients of 0.6 and RRMSE below 6.99%. The hyperspectral analyses presented a potential for the evaluation of leaf degradation in lisianthus floral stems in different pulsing solutions used in the postharvest.

Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha−1). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400–680 nm). Acceptable models were obtained (R2 > 0.70 and RMSE 0.81 and RMSE

The chemical weathering intensity in Antarctica is underestimated. As the chemical weathering intensity increases, hydrological, geochemical and geophysical changes occur in the different environmental spheres and at their interfaces through reactions and energy flows. Thus, once chemical weathering rates are understood and estimated, they can be used to predict and assess changes and trends in different environmental spheres. Few studies on the chemical weathering intensity have been performed in Antarctica. We used radiometric and magnetic properties associated with terrain attributes and the chemical degree of alteration of the igneous rock to model the chemical weathering intensity in Maritime Antarctica by using machine learning. Then, we related the chemical weathering intensity and geophysical variables with periglacial processes. To do this, gamma-spectrometric and magnetic readings were carried out using proximal-field sensors at 91 points located on different lithologies in a representative area of Maritime Antarctica. A qualitative analysis of chemical alteration for the different lithologies was carried out based on field observations and rock properties, and the levels of the chemical weathering degree were established. The geophysical data associated with terrain attributes were used as input data in the modeling of the weathering intensity. Then, the levels of the rock weathering degree were used as the “y” variable in the models. The results indicated that the C5.0 algorithm had the best performance in predicting the weathering intensity, and the most important variables were eTh, 40K, 40K/eTh, 40K/eU, the magnetic susceptibility and terrain attributes. The contents of radionuclides and ferrimagnetic minerals in different lithologies, concomitantly with the intensity at which chemical weathering occurs, determine the contents of these elements. However, the stability and distribution of these elements in a cold periglacial environment are controlled by periglacial processes. The chemical weathering intensity prediction model using gamma-spectrometric and magnetic data matched the in situ estimate of the chemical degree of alteration of the rock. The pyritized andesites showed the highest intensities of weathering, followed by tuffites, diorites, andesitic basalts and basaltic andesites, and the lowest weathering intensity was shown by undifferentiated marine sediments. This work highlighted the suitability of using machine learning techniques and proximal-field sensor data to study the chemical weathering process on different rocks in these important and inhospitable areas of the cryosphere system.

Mapping soil fertility attributes at fine spatial resolution is crucial for site-specific management in precision agriculture. This paper evaluated the performance of mobile measurements using visible and near-infrared spectroscopy (vis–NIR) to predict and map key fertility attributes in tropical soils through local data modeling with partial least squares regression (PLS) and artificial neural network (ANN). Models were calibrated and tested in a calibration area (18-ha) with high spatial variability of soil attributes and then extrapolated in the entire field (138-ha). The models calibrated with ANN obtained superior performance for all attributes. Although ANN models obtained satisfactory predictions in the calibration area (ratio of performance to interquartile range (RPIQ) ≥ 1.7) for clay, organic matter (OM), cation exchange capacity (CEC), base saturation (V), and exchangeable (ex-) Ca, it was not repeated for some of them when extrapolated into the entire field. In conclusion, robust mappings (RPIQ = 2.49) were obtained for clay and OM, indicating that these attributes can be successfully mapped on tropical soils using mobile vis–NIR spectroscopy and local calibrations using ANN. This study highlights the need to implement an independent test to assess reliability and extrapolability of previously calibrated models, even when extrapolating the models to neighboring areas.

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

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 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 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.

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.

We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.

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.

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.

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: 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.

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.

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.

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.

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.

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.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%.

Soil fertility variability management is one of the pioneering and important areas in which Precision Agriculture has been commercially applied. Consequently, the objective of this work was to predict soil chemical attributes through spectral responses. The 1,000 ha study area used for this report was located in Uberlândia, Minas Gerais State, Brazil. Thirty sampling points were established, at which the soil was collected at 3 different depths. The samples were chemically and physically analyzed and the radiometric data obtained in the 400 – 2500 nm range. Multiple regression equations were generated for sum of bases, cation exchange capacity, base saturation, aluminum saturation, pH, P, K, Ca, Mg, Al, and H, all using 60 soil samples. H, Al, m%, and pH were found to have R2 values less than 0.50. Equations with an R2 > 0.50 for the other attributes were tested for the 30 unknown soil samples, and the estimated values were obtained. These values were then compared with those determined by conventional analysis. The coefficients of correlation were higher than 50% for all attributes except P and V%. Results indicated that determining chemical attributes with models that are specific for the region is feasible. Uma das primeiras e mais importantes áreas nas quais a Agricultura de Precisão está sendo comercialmente aplicada é o manejo da variabilidade da fertilidade do solo. Desta forma, o objetivo deste trabalho foi predizer o teor dos atributos químicos do solo através da sua resposta espectral. A área de estudo de 1000 ha localiza-se em Uberlândia, Estado de Minas Gerais. Estabeleceu-se 30 pontos de amostragem nos quais o solo foi coletado em 3 profundidades. As amostras foram analisadas química e fisicamente e, os dados radiométricos obtidos com um sensor em laboratório na faixa de 400 – 2500 nm. Equações de regressão múltipla foram geradas para soma de bases, CTC, saturação por bases, saturação por alumínio, pH, P, K, Ca, Mg, Al e H usando 60 amostras de solo. H, Al, m% e pH obtiveram R2 < 0,50. As equações com R2 > 0,50 para os outros atributos foram testadas para as 30 amostras de solo desconhecidas obtendo-se os valores estimados. Posteriormente, estes valores foram comparados com aqueles determinados pela análise convencional. Os coeficientes de correlação foram maiores que 50% para todos os atributos exceto P e V%. Os resultados indicam que é possível determinar atributos químicos com modelos específicos para a região.

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.A importância de compreender a variabilidade espacial do solo está conectada ao planejamento do manejo das culturas. Este entendimento faz com que seja possível tratar o solo não como uma entidade uniforme, mas variável, e permite o gerenciamento de sítios específicos para aumentar a eficiência de produção, que é o objetivo da agricultura de precisão. Questões relacionadas com a otimização do intervalo de amostragem do solo se faz necessário para a realização das recomendações de adubações no Brasil. Os objetivos deste estudo foram: i) avaliar a variabilidade espacial dos principais atributos que influenciam as recomendações de adubação, usando amostras de solos georreferenciadas dispostas em padrões de grades de diferentes resoluções; ii) comparar os mapas espaciais gerados com o mapa padrão obtido com amostragem de 1 amostra ha-1, a fim de verificar a adequação da resolução espacial. Os atributos avaliados foram fósforo (P), potássio (K), matéria orgânica (MO), saturação por bases (V%) e argila. As amostras de solos foram coletadas numa grade de 100 × 100 m e georreferenciadas. Um desbaste foi realizado, criando-se uma grade com 1 amostra a cada 2,07, 2,88, 3,75 e 7,20 ha. Técnicas de geoestatística, como semivariograma e interpolação usando krigagem, foram utilizadas para analisar os atributos nas grades com diferentes resoluções. Esta análise foi realizada com o programa computacional Vesper. Os mapas criados por este método foram comparados utilizando-se a estatística kappa. Além disso, gráficos de correlação foram construídos plotando-se os valores observados pelos valores estimados utilizando-se a validação cruzada. Para P, K e V%, uma amostragem de resolução mais fina do que aquela usando 1 amostra ha-1 foi necessária, enquanto que para MO e argila, resoluções mais grosseiras de uma amostra ou dois em dois e três hectares, respectivamente, pode ser aceitável.

Soil 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 Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.A ciência do solo tem buscado desenvolver técnicas que contribuam para a melhor utilização e caracterização do solo; entre elas encontra-se a aplicação de técnicas de sensoriamento remoto. O uso de sensores terrestres na obtenção de dados espectrais de solos tem possibilitado a caracterização desses e o avanço de técnicas de quantificação de seus atributos. Para isso, a montagem de uma biblioteca espectral de solos se faz necessária. Uma biblioteca espectral deve ser representativa da variabilidade de solos de uma região. O presente trabalho teve por objetivo criar uma biblioteca espectral de distintos solos de algumas regiões agrícolas do Brasil. Para isso, foram coletados dados espectrais (sensor Fieldspec, 350-2.500 nm) dos horizontes de 233 perfis de solos das regiões de Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracaju, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); e Uberaba e Lagoa da Prata (MG). Posteriormente, foi realizada a análise de componentes principais (PCA) dos dados e a representação gráfica das curvas espectrais de cada perfil. A intensidade das curvas foi influenciada principalmente pelos teores de Fe2O3, argila, matéria orgânica e presença de minerais opacos. Latossolos, Nitossolos e Neossolos Quartzarênicos não apresentaram mudança no comportamento espectral das curvas de seus horizontes. Argissolos mostraram as curvas dos horizontes superficiais com maior intensidade de reflectância após os 2.200 nm. Cambissolos e Neossolos Litólicos apresentaram curvas de maior intensidade de reflectância nos horizontes pouco desenvolvidos. Gleissolos mostraram forma convexa de curva na região dos 350-400 nm. A PCA foi capaz de separar diferentes regiões de coleta de dados em função do material de origem. A componente principal (PC) 1 correlacionou-se com a intensidade de reflectância das amostras, e a PC2, com a inclinação entre a região do visível e a do infravermelho das amostras. O uso da biblioteca espectral como indicativo de possíveis classes de um solo mostrou ser ferramenta importante para a classificação de perfis.

With the development of remote sensing techniques, the understanding of soil spectral response with the purpose to assist soil mapping and estimate soil attributes, become necessary. For this, the objective of this work was to analyze the spectral response of eight soil profiles from Mogi-Guaçú (SP) region obtained in laboratory and correlate them with soil texture and their chemical and mineralogical characteristics. The soils were sampled in each horizon and radiometric data was obtained by a spectroradiometer in laboratory. It was possible to discriminate the spectral response of each soil and relate them with texture, organic matter and iron oxides content. The analysis of reflected electromagnetic energy permitted to evaluate the soils mineralogical compounds like iron oxides, kaolinite, gibbsite and 2:1 clay minerals. These results indicate that this technique can be used as an auxiliary method in soil studies and mapping. Com o desenvolvimento das técnicas de sensoriamento remoto, o entendimento da resposta espectral dos solos com a finalidade de auxiliar no mapeamento e na estimativa dos seus atributos torna-se necessária. Desta forma, este trabalho teve por objetivo analisar a resposta espectral de oito perfis de solos da região de Mogi-Guaçú (SP) obtida em laboratório, e correlacioná-la com a textura e suas características químicas e mineralógicas. Os solos foram amostrados por horizontes e os dados de reflectância foram obtidos em laboratório utilizando-se um espectroradiômetro. Foi possível diferenciar a resposta espectral de cada solo e correlacioná-la com a textura, conteúdo de matéria orgânica e teor óxido de ferro. A análise da energia eletromagnética refletida permitiu avaliar a composição mineralógica dos solos com relação aos óxidos de ferro, caulinita, gibbsita e minerais 2:1. Tais resultados indicam que a análise espectral pode ser utilizada técnica auxiliar no estudo e mapeamento dos solos.

Com a correção da acidez, nutrientes importantes para o cultivo do milho tornam-se mais disponíveis à planta, promovendo o aumento de produtividade. Objetivou-se com este estudo avaliar as alterações químicas ocorrentes em dois solos e na produção de matéria seca de milho pela aplicação de corretivos com diferentes graus de reatividade. Foi realizado um primeiro experimento em casa de vegetação, aplicando-se doses de três calcários comerciais, sendo um com reatividade média, denominado A (RE = 85,7 %; PRNT = 88,3 %), outro com reatividade baixa denominado B (RE = 83,7 %; PRNT = 76,2 %), e outro, denominado C (RE = 99,1 %; PRNT = 105,3 %) altamente reativo, em quatro cultivos sucessivos de milho. Os solos utilizados foram Neossolo Quartzarênico e Latossolo Vermelho. Os tratamentos foram dispostos inteiramente ao acaso, com quatro repetições, constando de três doses de calcário, calculadas para elevar a saturação por bases para 40, 60 e 80 %, além da testemunha. Paralelamente, um experimento de incubação foi realizado para verificar o efeito residual dos corretivos ao longo do tempo. A calagem aumentou os valores de pH, Ca2+ e Mg2+ e reduziu os teores de Al3+ trocável. O melhor crescimento do milho foi observado no Latossolo Vermelho. Neste solo, os calcários A e B foram mais eficientes na elevação do pH durante os quatro cultivos, enquanto o produto C teve menor eficiência na redução do Al3+ nos últimos ciclos, devido à sua maior reatividade. Os calcários A e C tiveram comportamentos semelhantes nos dois solos estudados. Houve queda na produção de matéria seca aérea com os cultivos sucessivos de milho. No experimento de incubação, os teores de Ca trocável foram maiores que os residuais.With acidity correction, important nutrients for the corn development become more available to the plants, increasing yield. The objective of this study was to evaluate chemical changes in two soils and corn dry matter yield as affected by lime application with different degrees of reaction. The experiment was carried out in a greenhouse on a Oxisol and a Quartzpsament. Three rates of three commercial limes were applied, one with mean reactivity A (RE = 85.7 %; PRNT = 88.3 %), another with low reactivity B (RE = 83.7 %; PRNT = 76.2 %) and C (RE = 99.1 %; PRNT = 105.3 %) with high reactivity, in four successive corn cycles. A randomized complete block design was used with four replications. The treatments consisted of three lime rates, calculated to increase base saturation to 40, 60 and 80 %, plus a control treatment. In parallel, another experiment was performed to verify the residual lime effect over time by incubation. Liming increased the values of pH, Ca2+ and Mg2+ and reduced Al3+. The best corn development was observed on the Oxisol. In this soil, the limes A and B were more efficient to increase the pH throughout the cycles, whereas product C had the lowest efficiency in Al3+ reduction in the last cycles due its higher reactivity degree. Limes A and C performed similarly in both soils. The shoot dry matter yield declined in the course of the successive corn cycles. In the incubation experiment, the exchangeable calcium content was higher than the residual.

ResumoOs sistemas convencionais de estimativa de safras são geralmente onerosos e demorados, portanto, é importante o desenvolvimento de métodos mais rápidos para a obtenção de dados. Propõe-se, neste trabalho, obter uma metodologia para estimar a produtividade agroindustrial da cana-de-açúcar pelo sensor orbital ETM+/LANDSAT 7 e utilizando-se dados reais áreas de produção de cana-de-açúcar na região de Paraguaçu Paulista, (SP), da variedade RB835486, de primeiro corte. Um banco de dados foi elaborado e escolheram-se talhões com épocas de plantio semelhantes, enquanto que para a coleta de dados espectrais foram testadas duas metodologias: na primeira, foram estabelecidas áreas de coleta de dados espectrais dentro dos talhões, e estes foram representados pelas respectivas médias aritméticas dos pixels amostrados e, na segunda, procedeu-se a uma classificação não supervisionada dos dados espectrais dos talhões que, por sua vez, foram representados pela média ponderada do valor dos pixels em sete classes espectrais distintas. As bandas B1, B2, B3, B4, B5 e B7 e seis índices de vegetação, foram utilizados para gerar modelos de regressão linear múltipla, visando à quantificação das características agroindustriais da cultura. Os resultados indicam que é possível estimar essas características através de dados espectrais orbitais, indicados por R2 = 0,69 para produtividade agrícola (Mg ha-1) e em média 0,58, para as características tecnológicas.AbstractConventional systems for crop yield projections are generally expensive and demand much time. Thus, it is important to develop quicker data obtaining methods. We have proposed a methodology for estimating the sugarcane agroindustrial yield by ETM + / LANDSAT 7 orbital sensor and also by collecting data from sugarcane production areas in Paraguaçu Paulista, São Paulo State, at fields of RB835486 variety in the first harvest. A database was established, and fields that were planted at the same dates were chosen. Two methods for spectral data collection were tested in order to identify best estimation results. In the first method, data collection areas were set within the spectral plots, and these were represented by the specific arithmetic average of the sampled pixels. In the second method, the spectral data went through a non-supervised classification of the plots, which were represented by the weighted average value of pixels in seven different spectral classes. The bands B1, B2, B3, B4, B5 and B7 and six vegetation indices were used to generate multiple linear regression models in order to qualify the crop agroindustrial characteristics. The results indicate that it is possible to estimate these characteristics drawing on orbital spectral data, indicated by R2 = 0.69 for crop yield (Mg ha-1) and a 0.58 average for the technological characteristics.

O objetivo deste trabalho foi integrar, em uma base de dados geoespacial, informações a respeito da distribuição dos solos na paisagem, bem como de seus atributos dentro de uma Reserva Particular do Patrimônio Natural (RPPN), situada no município de Lobato, Estado do Paraná. Todas as características de aspecto físico e morfológico obtidas foram incorporadas em um Sistema de Informações Geográficas (SIG). Diante dos dados das análises e do estudo de sua distribuição espacial, as unidades fisiográficas passaram a constituir unidades resultando na produção de mapas.The objective of this study was to integrate, into a geospatial database, information regarding the distribution of soils inthe landscape, as well as their attributes inside a Natural Heritage Private Reserve (RPNP) located in the city of Lobato. All physical and morphologic characteristics were incorporated into a Geographical Information System (GIS). Based on the analyzed data and the study oftheir spatial distribution, these physiographic units were eventually mapped.

Parte da variabilidade dos índices de produção agrícola está associada com as características do solo e da paisagem. Dessa forma, práticas de manejo, como a adubação, devem levar em consideração esta variabilidade. O sensoriamento remoto é uma ferramenta que pode fornecer, de maneira rápida, informações para o manejo do solo, pois relaciona a radiação eletromagnética com os atributos do solo. Assim, este trabalho teve como objetivo avaliar o comportamento espectral, em dois níveis de aquisição de dados (terrestre e orbital), de diferentes classes de solos ao longo de toposseqüências na região de São Carlos e Ibaté, SP. Para isso, amostras de terra georreferenciadas foram coletadas em 319 pontos, em três profundidades. Em seguida, obtiveram-se os dados radiométricos em laboratório, na faixa espectral entre 450 e 2.500 nm. Os mesmos locais amostrados na camada superficial, no campo, foram avaliados na imagem de satélite. A partir dos resultados obtidos, pode-se concluir que: (a) teores de areia grossa, argila e matéria orgânica, e cor tiveram relação com a reflectância dos solos; (b) ao longo das vertentes ocorrem variações nos dados espectrais dos solos; e (c) solos da mesma ordem taxonômica, porém com classes texturais diferentes, apresentam diferentes comportamentos espectrais, podendo ser discriminados por sensoriamento remoto.Part of agricultural production index variability is associated with soil and landscape characteristics. Management practices such as fertilizer application should therefore take the soil spatial variability into account. Remote sensing is a tool that can provide faster information for soil management because it relates electromagnetic radiation with soil attributes. Thus, this study aimed at evaluating the spectral response, at two data acquisition levels (terrestrial and orbital), of different soil classes across toposequences in the region of São Carlos and Ibaté, SP. For this purpose, georeferenced soil samples were collected at 319 points and three depths, and radiometric data were obtained in the laboratory using a spectroradiometer (spectral band 450-2500 nm). The same points sampled in the field in the surface layer were evaluated by the satellite image (ASTER). Based on the results it was possible to conclude that (a) coarse sand, clay, organic matter and color were strongly related with soil reflectance; (b) soil spectral data vary along the declivities; (c) soils of the same taxonomy order, but of different clay classes, have different spectral performances and can be discriminated by remote sensing.

Com vistas em avaliar o comportamento da linha do solo para diferentes classes de solos, este trabalho foi realizado, utilizando-se dados de reflectância por meio de um espectrorradiômetro em laboratório. Amostras representativas dos horizontes superficiais e subsuperficiais de 18 classes coletadas em uma área piloto no Município de Rafard, sudoeste de São Paulo, foram secas, peneiradas e acondicionadas para leitura espectral. Os dados foram tabulados, para simularem as bandas 3 e 4 do TM-Landsat e construção dos gráficos. Os resultados demonstraram que as linhas de solo obtidas para as amostras de ambas as camadas comportaram-se de maneira semelhante, estando dispostas ortogonalmente aos eixos de um gráfico, com valores de R² próximos a 1,0 para todas as classes avaliadas. Não se observou uma única linha de solo, embora os dados revelem que cada solo apresenta uma linha individual e característica. De modo complementar, destacou-se a utilização deste índice como método auxiliar na discriminação de classes de solos, uma vez que os solos com textura arenosa e com menores teores de Fe total discriminaram-se daqueles mais argilosos e com maiores teores de Fe, além de apresentarem valores de reflectância mais elevados. Por fim, constatou-se que a matéria orgânica não constituiu fator determinante no comportamento espectral em relação à linha do solo.The aim of this study was to evaluate the behavior of the soil line for different soil classes by using reflectance data with a spectroradiometer in the laboratory. Representative samples of the surface and subsurface horizons of 18 soil classes collected in a pilot area in Rafard county, southwest São Paulo state, were dried, sieved and accomodated for spectral reading. Data were computed in order to simulate TM-Landsat 3 and 4 bands and to draw the graphs. The findings showed that the soil lines obtained for the samples of both soil layers behave similarly and are distributed orthogonal to the graph axes with R² values at about 1.0 for all classes evaluated. It was observed that there is not a unique soil line, and the data suggest that each soil has an individual line and features. In addition, the use of such an index can be an auxiliary method for discriminating soil classes, since sandy soils, with a lower iron content were different from the more clayey ones with a higher iron content, besides presenting higher reflectance values. Finally, it was observed that the organic matter was not a determinant factor regarding the spectral behavior in relation to the soil line.

O presente estudo teve por objetivo caracterizar diferentes fases de intemperismo de um solo e relacioná-las com seu comportamento espectral. Um perfil pedológico desenvolvido sobre diabásio da região de Capivari-SP, foi descrito morfologicamente, identificando-se seis fases de alteração. Os atributos analisados foram granulometria, composição química e mineralógica. A reflectância espectral do solo foi avaliada em laboratório através de espectrorradiômetro na faixa de 300 a 2500 nm. O perfil apresentou grau de intemperismo moderado, o que foi evidenciado pela alta relação silte/argila observada abaixo do horizonte Bi. Os horizontes subsuperficiais também apresentaram alto teor de nutrientes, especialmente P, Ca e Mg, que estavam relacionados com a presença em subsuperfície de saprolito com razoável reserva de minerais intemperizáveis. A evolução dos minerais primários iniciou pela formação de óxidos de ferro e de argilas 2:1, como vermiculita ou vermiculita-esmectita, que foram transformadas em caulinita e gibbsita em direção ao topo do perfil. Na medida em que ocorreram alterações na composição mineralógica no perfil, foram verificadas variações nos dados espectrais. Basicamente a reflectância foi influenciada diferenciadamente pela ocorrência de óxidos de ferro, diferentes tipos de argilas e minerais primários como piroxênios e magnetita.The aim of this study was to characterize soil materials with different degrees of weathering and then associate their composition with their spectral behavior. One pedological profile developed from diabase was studied in Capivari-SP, Brazil. The morphological description allowed to separate six phases of rock-soil alteration. Afterwards, granulometry, chemical and mineralogical analysis were carried out. The soil spectral reflectance was evaluated with a laboratory spectroradiometer using the wavelength range of 300 to 2500 nm. The profile was moderately weathered as evidenced by a high silt/clay ratio, found below the Bi horizon. Subsurface horizons also presented high nutrient status, especially for P, Ca and Mg, which was related to the occurence of saprolite with an abundance of easily weathered minerals. The mineral evolution began with the precipitation of iron oxides and 2:1 clay minerals, like vermiculite and vermiculite-smectite, and this led to the formation of kaolinite and gibbsite in the upper part of the soil profile. Spectral behavior generally was in conformity with their mineralogical composition, pointing out to the presence of iron oxides, various groups of clays and primary minerals, such as pyroxenes and magnetite.

Os levantamentos pedológicos são amplamente utilizados nos mapeamentos de solo por serem métodos confiáveis, no entanto, apesar de tal vantagem são demorados e trabalhosos. Dentro deste contexto, surge o sensoriamento remoto como uma técnica rápida e promissora capaz de auxiliar nos levantamentos, de forma a tornar o processo mais dinâmico. O objetivo deste trabalho foi avaliar a possibilidade de discriminação de cinco classes de solos localizadas no planalto de Apucarana por meio de suas respostas espectrais. Foi estabelecido um grid de 500 m x 500 m a partir do qual coletaram-se amostras de 0-0,2 m e de 0,8-1,0 m de profundidade. As reflectâncias foram obtidas com o FiedSpec 3 jr, na faixa de 350 a 2500 nm. Equações Discriminantes e simulações foram geradas a partir das respostas espectrais das amostras de solo. Das 88 variáveis avaliadas apenas 8 foram selecionadas pelo procedimento STEPDISC para fazerem parte dos modelos. As equações discriminantes geradas foram testadas, obtendo-se matrizes de confusão, as quais apresentaram acerto acima de 70% para cada classe de solo. Da mesma forma, equações discriminantes simuladas foram geradas, obtendo-se melhores resultados de reclassificação quando utilizados dados que fizeram parte da geração do modelo (60%) em comparação com os dados independentes do modelo (40%). Os resultados mostraram que a análise discriminante foi capaz de reclassificar as amostras de solo dentro de suas respectivas classes de forma satisfatória, sendo possível empregar-la em ambientes retrabalhados.

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 cross-tabulation 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 integrar, em uma base de dados geoespacial, informações a respeito da distribuição dos solos na paisagem, bem como de seus atributos dentro de uma Reserva Particular do Patrimônio Natural (RPPN), situada no município de Lobato, Estado do Paraná. Todas as características de aspecto físico e morfológico obtidas foram incorporadas em um Sistema de Informações Geográficas (SIG). Diante dos dados das análises e do estudo de sua distribuição espacial, as unidades fisiográficas passaram a constituir unidades resultando na produção de mapas.

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.

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.

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.