RESUMO

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 <1.41 g kg-1), the most robust of which were those generated from spectra in the visible (400-680 nm) and red-edge (680-750 nm), with values of R2 > 0.81 and RMSE <1.24 g kg-1. An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.

RESUMO

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.

Assuntos

RESUMO

Wetlands are important ecosystems characterized by redoximorphic environments producing typical soil forming processes and organic carbon accumulation. Assessments and management of these areas are dependent on knowledge about soil characteristics and variability. By reflectance spectroscopy, information about soils can be obtained since their spectral behaviors are directly related to their chemical, physical, and mineralogical properties reflecting the pedogenetic processes and environment conditions. Our aims were: (a) to characterize the main soil classes of wetlands regarding their spectral behaviors in VIS-NIR-SWIR (350-2500 nm) and relate them to pedogenesis and environmental conditions, (b) to determine spectral ranges (bands) with greater expression of the main soil properties, (c) to identify spectral variations and similarities between hydromorphic soils from wetlands and other soils under different moisture conditions, and (d) to propose spectral models to quantify some chemical and physical soil properties used as environmental quality indicators. Nine soil profiles from the Pantanal region (Mato Grosso State, Brazil) and one from the Serra do Espinhaço Meridional (Minas Gerais State, Brazil) were investigated. Spectral morphology interpretation allowed identifying horizon differences regarding shape, absorption features and reflectance intensity. Some pedogenetic processes of wetland soils related to organic carbon accumulation and oxide iron variation were identified by spectra. Principal Component Analysis allowed discriminating soils from wetland and outside this area (oxidic environment). Quantification of organic carbon was possible with R2 of 0.90 and low error. Quantification of clay content was masked by soils with organic carbon content over 2% where it was not possible to quantify with high R2 and low error both properties when dataset has soil samples with high organic carbon content. By reflectance spectroscopy, important characteristics of wetland soils can be identified and used to distinguish from soils of different environments at low costs, reduced time, and with environmental quality.

Assuntos

RESUMO

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.

Assuntos

RESUMO

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.(AU)

Assuntos

RESUMO

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 em uma área com dimensão de 2500 ha, a partir do qual foram coletadas amostras a 0-0,2 m e 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 resultados mais significativos para reclassificação quando utilizados dados que fizeram parte da geração do modelo (60%) em comparação com os dados independentes do modelo (40%). As respostas espectrais das amostras de solo empregadas na análise discriminante foram capazes de dar subsídio para separação das cinco classes de solo da área de estudo, comprovando ser uma ferramenta valiosa mesmo em condições de elevada variabilidade pedológica e de atributos como em regiões transicionais.

The pedological surveys are widely used in soil mapping because they are reliable methods, however, although this advantage are time consuming and laborious. Within this context, remote sensing appears as a quickly and promising technique able to assist in the surveys in order to make the process more dynamic. The objective this work was to evaluate the possibility of discrimination of five classes of soils located in the plateau of Apucarana through their spectral responses. Was established a grid of 500 m x 500 m in an area with dimensions of 2500 ha, from which samples were collected at 0 to 0.2 and of 0.8 to 1.0 m deep. The reflectances were obtained with the FiedSpec 3 JR, in the range 350 to 2500 nm. Discriminant equations and simulations were generated from the spectral responses of soil samples. Of the 88 variables evaluated, only 8 were selected by the procedure STEPDISC to be part of the models. The discriminant equations generated were tested, resulting in confusion matrices, which showed accuracy above 70% for each class of soil. Likewise, simulated discriminant equations were generated, obtaining most significant results for reclassification when used data that were part of the model generation (60%) in comparison with the model independent data (40%). The spectral responses of soil samples used in discriminant analysis were able to give support for separation of five classes of soil in the study area, proving to be a valuable tool even in conditions of high pedological and attribute variability as in transitional regions.

Assuntos