Orbital and laboratory spectral data to optimize soil analysis

Abstract

Traditional soil analyses are time-consuming with high cost and environmental risks, thus the use of new technologies such as remote sensing have to be estimulated. The purpose of this work was to quantify soil attributes by laboratory and orbital sensors as a non-destructive and a nonpollutant method. The study area was in the region of Barra Bonita, state of São Paulo, Brazil, in a 473 ha bare soil area. A sampling grid was established (100 × 100 m), with a total of 474 locations and a total of 948 soil samples. Each location was georeferenced and soil samples were collected for analysis. Reflectance data for each soil sample was measured with a laboratory sensor (450 to 2,500 nm). For the same locations, reflectance data was obtained from a TM-Landsat-5 image. Multiple linear regression equations were developed for 50% of the samples. Two models were developed: one for spectroradiometric laboratory data and the second for TM-Landsat-5 orbital data. The remaining 50% of the samples were used to validate the models. The test compared the attribute content quantified by the spectral models and that determined in the laboratory (conventional methods). The highest coefficients of determination for the laboratory data were for clay content (R 2 = 0.86) and sand (R 2 = 0.82) and for the orbital data (R 2 = 0.61 and 0.63, respectively). By using the present methodology, it was possible to estimate CEC (R 2 = 0.64) by the laboratory sensor. Laboratory and orbital sensors can optimize time, costs and environment pollutants when associated with traditional soil analysis. Key words: remote sensing; soil attributes; soil reflectance DADOS ESPECTRAIS ORBITAIS E DE LABORATÓRIO NAOTIMIZAÇÃO DA ANÁLISE DO SOLO RESUMO: Analises de solos tradicionais consomem tempo com alto custo e riscos ambientais. Dessa forma, o uso de novas tecnologias como o sensoriamento remoto tem sido estimulado. O objetivo do trabalho foi quantificar atributos dos solos por um método não destrutivo e não poluente utilizando sensor de laboratório e orbital. A área de estudo localiza-se na região de Barra Bonita, Brasil, com 473 ha com solo exposto. Foi estabelecida uma malha de amostragem (100 m × 100 m) com 474 pontos de amostragem e um total de 948 amostras de solos. Os pontos de amostragem foram georreferenciados, e amostras de solos coletadas e enviadas para análises. Os dados de Reflectância foram obtidos para cada amostra de solo com o sensor em laboratório (450 a 2500 nm). Nos mesmos locais de amostragem de solos, foram obtidos dados de reflectância de imagens orbitais TM-Landsat-5. Equações lineares de regressão múltiplas foram desenvolvidas com 50% das amostras. Dois modelos foram desenvolvidos: o primeiro com dados de espectroradiometria de laboratório e o segundo para os dados orbitais da imagem TM Landsat-5. O restante das amostras, 50% foram utilizadas para validar o modelo. O teste comparou os valores dos atributos quantificados pelos modelos espectrais e os determinados em laboratório (método convencional). Os maiores coeficient...

Soil Spectral Mapping and Its Correlation With the Traditional Methodology

“…In Figure 7 is evident better randomized distribution than the previous one, and the largest gap between the location of groups within the geographical area (Minasny and McBratney 2016). The comparison between the groups obtained in this method and soil classes identified by conventional methodology obtained as recommended by Fiorio and Demattê (2009), is presented in Table 2. Classes G1 G2 G3 G4 G5 G6 G7 G8 Argissolo 3 7 1 2 3 6 The number of classes covered by these currently methods was greater than the existing map (Figure 8), even considering only the first categorical level of classification, and is therefore a relevant complement to the available information (Embrapa 2006 The greater diversification of soil classes distribution for groups is characterized in the class of Argissolos, which has at least one individual in each group.…”

“…There is a lack of studies regarding to lowland soils which is even more significant since others papers related to this topic (Nanni et al 2004;Chicati et al 2008;Fiorio and Demattê 2009) deal with specific and different environmental conditions of this. Accordingly, a lowland area with various soil classes was chosen to test the effectiveness of the method (Chicati et al 2010).…”

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

“…Although the Landsat bands have limited capacity for predicting soil properties (Section 4.4), the prediction results for SOM and clay are moderate and spatial patterns were predicted as expected. Other studies that predicted soil properties based on Landsat data [9,11,12] have shown similar or slightly better results, although comparison is difficult with only R 2 as a performance indicator. Moreover, when discussing these results, we must be aware that these are exclusively based on Landsat data while most digital soil maps used many more covariates.…”

In situ separation of soil types along transects employing Vis-NIR sensors: a new view of soil evaluation

“…Most studies in soil science involving remote sensing, have to do with the spectral characterisation of soils and the relationship with its components (BROWN et al, 2006;COHEN et al, 2007;COZZOLINO;MORON, 2003;DEMATTÊ, 2009;NANNI;DEMATTÊ, 2006;VISCARRA ROSSEL et al, 2009), which has been helping in soil discrimination. In the same way, conventional surveys use information about soil components obtained from chemical and physical analyses and from morphological evaluations as a means of establishing the classes which are present, and their distribution in the landscape.…”