ABSTRACT Understanding organic carbon and predominant humic fractions in the soil allows contributes to soil quality management. Conventional fractionation techniques require time, excessive sampling, and high maintenance costs. In this study, predictive models for organic carbon in humic substances (HS) were evaluated using hyperspectral data as an alternative to chemical fractionation and quantification by wet digestion. Twenty-nine samples of Neossolos Flúvicos (Fluvents) - A1, and 36 samples of Cambissolos (Inceptisols) - A2 were used. The samples were also analyzed jointly, creating a third sample group - A1&A2. Untransformed spectral reflectance factors were obtained using the FieldSpec Pro FR 3 hyperspectral sensor (350–2500 nm). Pre-processing techniques were employed, including Savitzky–Golay smoothing and first- and second-order derivative analysis. After selecting variables using the Backward method, which removes spectral variables that are not statistically significant for the regression. Estimation models were built by Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR). The spectral data were evaluated individually for soil classes A1 and A2, and jointly for A1&A2. The PLSR was more efficient than PCR, especially for the estimation models that used the first derivative of reflectance employing the three sample groups. For samples of A1, the best estimate was seen for humic acid (RPD = 6.09) and humin (RPD = 2.38); for A2, the best models estimated the OC in fulvic acid (RPD = 2.35) and humin (RPD = 2.51); and for the joint spectral data (A1&A2), the prediction was robust for humin only (RPD = 2.01). The most representative wavelengths were observed using the first derivative with PLSR and PCR, centred on the region between 1600 and 1800 nm. The first-derivative of reflectance calculated more-robust predictive models using PLSR than PCR. The best predictions occurred for organic carbon associated with humic acid in Neossolos Flúvicos, with fulvic acid in Cambissolos, and with humin in both soil classes.

RESUMO A textura tem grande importância no manejo dos solos, pois influencia fortemente o comportamento físico-hídrico e químico dos solos. É necessário, portanto, a determinação de tal textura, sendo a razão de bandas espectrais, uma alternativa rápida e precisa para este fim. O objetivo deste trabalho foi avaliar o uso de dados espectrais, adquiridos pelo sensor aerotransportado ProSpecTIR-VS, com resolução espacial de 1 m, na seleção de duas bandas para composição de um Índice por Diferença Normalizada, que permita estimar os atributos texturais do solo, além de elaborar o mapa textural do solo na imagem. Foram coletadas 64 amostras em áreas inseridas no perímetro irrigado Jaguaribe-Apodi, que se localiza na Chapada do Apodi, Bacia do Baixo Jaguaribe, cujas classes predominantes são Cambissolos. As amostras foram coletadas em solo exposto, tendo como base as imagens hiperespectrais do sensor aerotransportado ProSpecTIR-VS. O Índice por Diferença Normalizada (NDI) foi construído, realizando-se todas as possíveis relações normalizadas de bandas e os melhores índices foram selecionados com base no coeficiente de determinação (R²). Os resultados mais promissores de R² foram obtidos na estimativa da areia, com as bandas 1045 e 1323 nm, com R² de 0.5. Os baixos valores de R² podem ser explicados pela interferência na resposta espectral por materiais que estavam na superfície do solo, como resto de cultura, cascalho e da vegetação. A elaboração do mapa de areia utilizando o melhor modelo resultou em 82.1% dos pixels com valores entre 20 e 60% de areia, enquadrando-se entre mínimo e máximo dos teores de areia das amostras de solos.

ABSTRACT Texture is of great importance in soil management, as it strongly influences the physical, hydraulic and chemical behaviour of soils. It is therefore necessary to determine texture, with the spectral band ratio being a fast and precise alternative method for this purpose. The aim of this study was to evaluate the use of spectral data, acquired by the ProSpecTIR-VS airborne sensor with a spatial resolution of 1 m, in selecting two bands for building a Normalised Difference Index that allows the textural attributes of the soil to be estimated, besides preparing a texture map of the soil in the image. Sixty-four samples were collected from several areas inserted in the Jaguaribe-Apodi irrigated perimeter, which is located in the Chapada do Apodi, in the Lower Jaguaribe Basin, where the predominant soil classes are Cambisols. The samples were collected from exposed soil, based on the hyperspectral images of the ProSpecTIR-VS airborne sensor. The Normalised Difference Index (NDI) was constructed, carrying out all possible normalised band ratios, with the best indices selected based on the coefficient of determination (R²). The most promising results for R² were obtained when estimating sand in the 1045 and 1323 nm bands, with an R² of 0.5. The low values for R² can be explained by interference in the spectral response from materials on the soil surface, such as crop residue, gravel and vegetation. Preparing the sand map using the best model resulted in 82.1% of the pixels having values between 20 and 60% sand, falling between the minimum and maximum sand content of the soil samples.

RESUMO O estudo minucioso de dados hiperespectrais pode ser um forte aliado para o diagnóstico nutricional de plantas e otimizam o manejo de insumos agrícola. Este estudo caracterizou as variações dos fatores de reflectância em folhas de algodoeiro (Gossypium hirsutum L.) da cultivar BRS 293, submetidas a diferentes níveis de adubação de N e K. Foram cultivadas 166 plantas submetidos a quatro doses de N e K, com vinte repetições e seis controles. Cada tratamento se referiu a um nível de adubação: 50, 75, 100 e 125% da quantidade recomendada para ambos macronutrientes em cada etapa do ciclo fenológico. O espectrorradiômetro utilizado em laboratório foi o FieldSpec Pro FR 3® com resolução espectral de 1 nm e cuja faixa de operação se estende de 350 a 2500 nm. Em ambos os tratamentos, a ACP permitiu identificar os comprimentos de onda agrupados em parâmetros como o brilho, a clorofila e a umidade das folhas. A adubação de N e K provocou alterações significativas nos fatores, onde a maior diferenciação entre as doses se deu aos 790 e 1198 nm. Os comprimentos entre 550 e 700 nm e em 1390 e 1880 nm se mostraram, respectivamente, as mais promissoras para explicar a variância dos níveis nutricionais de N e K sobre a cultura do algodoeiro.

ABSTRACT The detailed study of hyperspectral data that optimise the management of agricultural inputs can be a powerful ally in the nutrient diagnosis of plants. This study characterised variations in the reflectance factors of cotton leaves (Gossypium hirsutum L.) of the BRS 293 cultivar, submitted to different levels of N and K fertilisation. A total of 166 plants were submitted to four doses of N and K, with twenty replications and six controls. Each treatment represents one level of fertilisation: 50, 75, 100 and 125% of the recommended amount of both macronutrients at each stage of the phenological cycle. The spectroradiometer used in the laboratory was the FieldSpec Pro FR 3® with a spectral resolution of 1 nm and an operating range that extends from 350 to 2500 nm. In both treatments, PCA allowed wavelengths to be idenbtified grouped by such parameters as brightness, chlorophyll and leaf moisture. The N and K fertilisation caused significant changes in the factors, where the greatest difference between doses was seen at 790 and 1198 nm. The wavelengths between 550 and 700 nm and at 1390 and 1880 nm were, respectively, the most promising for explaining the variance in nutrient levels of N and K in cotton.

RESUMO Em virtude do crescimento exponencial no volume de dados considerados nas tomadas de decisões inteligentes, o sensoriamento remoto hiperespectral (SRH) tem indubitavelmente trazido maior domínio sobre os cultivos agrícolas porque ultrapassa o paradigma de pouca informação disponível sobre os alvos. Nesta revisão do estado da arte do SRH foram agrupadas visões complementares sobre o uso de sensores e técnicas analíticas na agricultura desta última década. Foram citadas tecnologias de ponta e as tendências de pesquisa associadas a cada nível de coleta de dados. Ainda há muito a avançar nas ciências agrárias, no entanto, os profissionais da agricultura de precisão estão devotados aos insights de aplicações valiosas com a maior disponibilidade de dados hiperespectrais. Neste sentido, esta revisão está organizada da seguinte maneira: A seção 1 contextualiza o leitor e conceitua fundamentos para o sensoriamento remoto. A segunda seção discorre sobre os tipos de sensores e suas resoluções. A seção 3 traz quatro subseções que apresentam aplicações recentes destas tecnologias segundo o nível de aquisição. Finalmente, a quarta seção oferece ao leitor a discussão sobre as tendências positivas alcançadas no manejo de vegetação, solos e corpos hídricos nos últimos dez anos, bem como as necessidades e desafios para a próxima década.

ABSTRACT In view of the exponential growth in the volume of data that is considered in intelligent decision-making, hyperspectral remote sensing (HRS) has, without doubt, brought greater dominance over agricultural crops as it goes beyond the paradigm of little information being available about the targets. In this review of the state of the art of HRS, complementary views on the use of sensors and analytical techniques in agriculture over the last decade are grouped together. State-of-the-art technologies, and research trends associated with each level of data collection are cited. There is still a long way to go in the agricultural sciences; however, specialists in precision agriculture are devotees of the valuable insights offered with the increased availability of hyperspectral data. In this respect, this review is organised as follows: Section 1 helps the reader to contextualise and conceptualise the basics of remote sensing; the second section discusses the types of sensors and their resolutions; section 3 presents four subsections that show recent applications of these technologies according to their level of acquisition; finally, the fourth section offers the reader a discussion on the positive trends achieved in managing vegetation, soils and waterbodies over the last ten years, as well as the needs and challenges of the next decade.