Mapping topsoil texture by satellite image and relief

O planeta terra tem grande dimensão, e seus recursos naturais precisam ser mapeados e conhecidos para nortear políticas públicas. O solo é um destes importantes recursos. O seu conhecimento passa pela caracterização e mapeamento pedológico e/ou de seus atributos. Para o adequado monitoramento, é necessário o conhecimento em escala detalhadas. Isto demanda recursos humanos, altos custos financeiros e de logística. Fato este ainda difícil de se atingir. Logo, é preciso investir em tecnologias que auxiliem na rápida obtenção de informações de qualidade, à baixo custo. Tendo em vista as áreas agrícolas da região de estudo, os objetivos deste trabalho foram: (i) definir uma metodologia que identifique em imagens de satélite, locais de o solo exposto; (ii) Mapear os teores granulométricos através de imagens de satélite e atributos do relevo, utilizou-se das imagens compostas do tópico (i). A área de estudo localiza-se na região de Araraquara, São Paulo, Brasil, com dimensão de 14.614 km2. Dentro desta área foram demarcados 952 pontos para coleta de amostras de terra na camada superficial, as quais foram georreferenciadas e analisadas granulometricamente em laboratório. Sua demarcação seguiu os preceitos do método da topossequência com o intuito de representar a variabilidade da região. Foram obtidas imagens do satélite Landsat 5 (sensor TM) multitemporais as quais foram processadas e transformadas em reflectância. As amostras de terra coletadas em campo passaram por sensor em laboratório (400-2500 nm), os espectros laboratoriais foram utilizados para validar aqueles obtidos nas imagens de satélite. Para tanto, nos locais onde foram coletadas as amostras, foram extraídos os dados espectrais dos pixels perfazendo os gráficos das curvas espectrais. Estas foram comparadas com os dados de obtidos em laboratório simulados. Feita a correlação, as imagens passaram por processos de eliminação de objetos que não fossem solo. Todas as imagens multitemporais foram finalizadas contendo apenas solo exposto, as quais dentro do software R foram sobrepostas e gerou-se uma imagem composta, com apenas solo exposto. Os resultados mostraram que as curvas espectrais de laboratório foram extremamente semelhantes aos das imagens de satélite, seguindo a lógica das variações texturais. Além disso, as técnicas de componentes principais e relação entre bandas 3-4, 5-7, e correlação entre bandas (sendo a mais expressiva com r de 0,87 entre TM7), comprovaram que a imagem apresentou solo exposto. Se um usuário utilizar-se somente uma imagem para estudar solos, teria na faixa de 4% de solo exposto, porém utilizando a técnica de composição de imagens, atingiria 43%. Não obstante, se a área de estudo fosse 100 % com agricultura poderia atingir 95% de solo exposto. Num segundo momento, o trabalho comprova, com o modelo Cubist, que tanto por imagens de satélite quanto por relevo foi possível quantificar os teores de argila da área da camada superficial, atingindo R2 de ≈0,65. No entanto, a qualidade visual do mapa gerado por relevo é ruim. Porém, quando se integra dados de imagens, relevo e geomorfologia, este resultado é de 0,72 e apresenta o melhor resultado visual.Planet Earth has great dimension, and its natural resources has to be mapped and monitored, looking towards correct decisions. Soil is one of these important resources. Know soils is related with its caracterization and mapping by pedological and attributes recognition. For soil monitoring, its necessary maps in large scale, which demand man power and high cost. Thus, its necessary to invest in geotechnologies, to reach the goal faster and low cost. The objective of this work was to determine a method to determine exposed soils in satellite images, even when have vegetation, taking in account a multitemporal dataset, in agricultural areas, where as in a given season will have exposed soils. b. quantify clay and sand contents by satellite images and relief attributes. The area is located in Araraquara, SP, Brazil, with a 14.614 km2 dimension. We collected soil samples all over the area with a total of 952 points and 0-20 cm depth, georeferenced, representative of the area. Samples were granulometric analysed and afterwards passed throgh a vis-nir-swir sensor (400-2500 nm). We collected multitemporal images from landsat satellite from september and october n the last 15 years. Images were atmospheric corrected and transformed into reflectance. Laboratory spectral data was used to validate pixels spectra information from satellite. We extracted all objects which were not soils from all images. Using R software, we merged the multitemporal images and performed a unique bare soil image. Also, we made processing on the DEM of the área reaching several soil attribute factors. Results indicated as follows: a. labortory spectral curves validated satellite data; b) principal componentes and relation between bands ¾ and 5/7 reached great R2 until 0,87 between laboratory and satellite data; d) a user could reach 1,21% of na image with bare soil, while with our method could reach 43% in the entire image. On the other hand, if the user have only agriculture area, could reach until 95% with bare soil. In a second step of this work, we prove that by regression tree statistics, clay and sand content can be quantified by satellite images with a 0,62 of R2, as also with terrain atributes. On the other hand, when we associate image spectral data with terrain atributes, we can reach 0,72 on clay quantification. Despite this, the visual aspecto of data, is better using image data than relief , which presented more noise. Another conclusion, is that images could substitute geology information in the models. This work can considerably assist pedologists, farmers and environment professionals on soil monitoring

Detecção de limites de solos por dados espectrais e de relevo

Existe a necessidade de avaliar a importância do relevo associado aos parâmetros espectrais de solos como base no mapeamento. O objetivo deste trabalho foi determinar um método de detecção de limites de solos por meio da interação de dados espectrais e formas de relevo. Foram percorridas 14 topossequências representativas de uma área de 13.000 ha próxima dos municípios de São Carlos e Araraquara, SP. As amostras foram caracterizadas pelos métodos convencionais de análise química e granulométrica. Posteriormente, foram obtidos dados espectrais de 400 a 2.500 nm. As informações do relevo foram obtidas pelo emprego de técnicas de geoprocessamento. Geraram-se o modelo digital de elevação do terreno e os mapas de declividade, de curvatura, de índice topográfico composto e de Potencial de Densidade de Drenagem. Ainda, procedeu-se à validação dos métodos pontual e espacial. Na primeira validação, os pontos classificados nas topossequências foram tomados como verdadeiros e contrastados com as informações contidas no mapa de solo pré-existente, com os dados de relevo e com os dados espectrais agrupados. Na validação em nível espacial, procuraram-se avaliar em que locais os diferentes métodos indicavam mudanças nos limites dos solos e comparar com as observações reais. Verificou-se que a análise de agrupamento com cluster evidenciou-se eficiente na discriminação das unidades de solos em topossequência, quando utilizados parâmetros espectrais do solo. Já o conjunto de parâmetros de relevo isoladamente não foi o mais adequado

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

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

Espectroscopia VIS-NIR-SWIR na avaliação de solos ao longo de uma topossequência em Piracicaba (SP)

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

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management

Digital soil mapping using reference area and artificial neural networks.

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

Is it possible to map subsurface soil attributes by satellite spectral transfer models?

It is impossible to make pedological maps without understanding subsurface attributes. Several strategies can be used for soil mapping, from a tacit knowledge to mathematical modeling. However, there are still gaps in knowledge regarding how to optimize subsurface mapping. This work aimed to quantify subsurface soil attributes using satellite spectral reflectance and geographically weighted regression (GWR) techniques. The study was carried out in Sao Paulo, Brazil, in an area spanning 47,882 ha. Multitemporal satellite images (Landsat-5) were initially processed in order to retrieve spectral reflectance from the bare soil surface. Based on a toposequence method, 328 points were then distributed across the area (at depths between 0 and 20 cm and 80 and 100 cm) and analyzed for their soil chemical and physical attributes (including the reflectance spectra (400 to 2500 run)) in the laboratory. We achieved 67.72% of bare soil for the whole study area, with the remaining 32.28% of the unmapped surface being filled by kriging interpolation. All 328 samples were modeled using surface (Landsat-5 TM spectral reflectance) and subsurface (acquired in the laboratory) data, reaching up to 0.72 RLi. The correlation between the spectra of both depths was significant and the soil attributes prediction reached an R!dj of validation above 0.6 for clay, hue, value, and chroma at 0-20 and 80-100 cm depths. The satellite soil surface reflectance allowed the estimation of soil subsurface attributes. These results demonstrate that diagnostic soil attributes can be quantified based on spectral pedotransfer (SPEDO) functions to assist digital soil mapping and soil monitoring. Despite our efforts to determine soil subsurface properties using digital soil mapping approach, this task still need considerable refinement. Thus, research must continue to aggregate outcomes from other techniques343269279CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão temnão tem2014/22262-0; 2016/26124-6; 2016/01597-