Luis Clenio Jario Moreira

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers.

To ensure high productivity, nutritional adjustments in fig cultivation are necessary during its different development stages. The objective of this study was to evaluate biomass and nutrient accumulation by fertigated fig trees, ‘Roxo de Valinhos’ cultivar, in calcareous soil under semi-arid climate conditions.The experiment was conducted at the Chapada do Apodi, state of Ceará, Brazil, from 2022 to 2023, using a randomized block design with split plots. The main plots were composed of three fertilization doses (D1 - 50%, D2 - 100%, and D3 - 150%) applied as top dressing through fertigation, and subplots by three sampling times: 58, 94, and 129 days after planting (DAP). Fig trees showed continuous growth in all three evaluated stages, regardless of fertilization dose. Theorder of biomass accumulation was:leaves>stems>fruits. The order of total macronutrient accumulation at 129 DAP, regardless of fertilization dose, was: Ca>K>N>Mg>P>S. In general, 50% or more of nutrients were accumulated after 94 DAP, with the exception of Mg. The majority of nutrients were accumulated in the vegetative organs (stem + leaves). At the end of the experiment, fruits showed the following decreasing order of macronutrient accumulation: K>N>Ca>Mg>P>S.

Desertification is the degradation process caused by climatic conditions and human activities that long term results loss of soil productivity and declive vegetation growth. Several index from vegetation, soil and climate are used to monitorig desertification, but few studies explore qualitative and quantitative index on desertification process on spatial and hierarchical scale. This work aims to identify and measure index related to increased risk of desertification on global, local and hierarchical scale using multinomial logistic regression models. Images from TM, ETM plus and OLI sensor between 1997 to 2018 in the dry and rainy to quantitative NDVI, TGSI, albedo, temperature, aridity index, evapotranspiration and precipitation on global spatial scale (Irauçuba Centro Norte) and local scale (Miraíma, Canindé, Irauçuba e Santa Quitéria). This IGSD calculated by geometric mean refers to score index, after to submit segmentation into 8 class of susceptibility to desertification (hierarchical scale). The results showed that the best models from local scale and rainy season. Temperature proved to be the most important variable increase risk of desertification on global, local and hierarchical scale. Therefore, the increase in the risk of desertification in the studied areas is due to human activities of deforestation, over grazing and fire. These factors results the reduction of vegetation cover and increase temperature, change microclimatic and results decline precipitation and more effect desertification problem. Desertifi cation is the degradation process caused by climatic conditions and human activities that results in loss of soilproductivity and decline in vegetation growth in the long term. Several indices related to vegetation, soil and climate are used to monitordesertifi cation, but few studies explore qualitative and quantitative aspects of indices on desertifi cation on spatial and hierarchical scale.This study aims to identify and measure indices related to increased risk of desertifi cation on global, local and hierarchical scales usingmultinomial logistic regression models. Images from TM, ETM+ and OLI sensor from 1997 to 2018 in the end of dry and rainy seasonswere used to quantify NDVI, TGSI, albedo, temperature, aridity index, evapotranspiration and precipitation on global spatial scale(Irauçuba Centro Norte) and local spatial scale (Miraíma, Canindé, Irauçuba and Santa Quitéria). GISD was calculated by geometricmean of weighted indices and segmented into 8 classes of susceptibility to desertifi cation (hierarchical scale). The results showed thatthe best models were obtained on local scale and for the end of the rainy season. Temperature proved to be the most important variablefor increased risk of desertifi cation on global, local and hierarchical scales. Therefore, the increase in the risk of desertifi cation in thestudied areas is due to human activities of deforestation, overgrazing and fi re. These factors contributed to reduction of vegetationcover and increase in temperature, changing the microclimatic, which led to decline in precipitation and worsening of desertifi cation.

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.

O município de Juazeiro do Norte, localizado na região do Cariri cearense, é uma cidade que se expandiu consideravelmente nas últimas décadas. O processo de urbanização acelerado, sem um planejamento ambiental, tem proporcionado aumento da temperatura do ar e da superfície, afetando diretamente o conforto térmico da população local. Deste modo, o estudo da espacialização e comportamento sazonal desse fenômeno é fundamental para tomadas de decisões nas políticas públicas da cidade. O objetivo deste trabalho, portanto, avaliar o efeito da dinâmica de uso do solo na temperatura superficial da cidade de Juazeiro do Norte visando identificar ilhas de calor e frescor em uma série temporal (2005 a 2020) de imagens da plataforma Landsat.. Para obtenção da série histórica da temperatura de superfície e NDVI foram utilizadas imagens do Landsat 5, sensor TM nas bandas 3, 4 e 6 e Landsat 8, sensor OLI, nas bandas 4, 5 e 10 adquiridas da United States Geological Survey (USGS). Como resultados, foram identificadas ilhas de calor que envolvem os bairros intensamente edificados e com classes de solos expostos, enquanto as ilhas de frescor compõem os corpos hídricos e a vegetação que os margeiam. A arborização da cidade com plantas perenifólias pode favorecer a redução da temperatura local principalmente nos meses de estiagem.

Regenerated fiber Bragg grating (RFBG) sensors for thermal aging at high temperatures from 600 °C to 1000 °C are produced and analyzed in this work. Seed FBGs are regenerated at 850 °C–900 °C and thermal characterized at the increasing steps of 50 °C–100 °C. Signal decay is measured from the reflected spectra until imminent erasure. The objective is to analyze the RFBG sensors lifetime above the regeneration temperature for long periods after annealing cycles with increasing thermal levels. Lifetime results from RFBG were reported to be a few hours above their regeneration temperature. Our results are significantly better. The sensors resisted for the time intervals of one to eight days up to 100 °C above the regeneration temperature. In addition, some sensors remained stable in terms of the spectral amplitude for more than four days in that thermal condition.

The objective of this study was to evaluate the assimilated contents of mineral elements, yield characteristics and fruit quality of ‘Goldex’ yellow melon fertigated with different doses of nitrogen (N) and potassium (K) in two years of cultivation in the region of Apodi Plateau-CE. The design was randomized blocks in a 4x3 factorial scheme with four N doses (0%, 50%, 100%, 150% of the recommended dose) and three K doses (0%, 100%, 150% of the recommended dose). In the shoots of the melon plant, the highest levels of N (N Fert.) and K (K Fert.) fertilization promoted an increase in the assimilated N content up to 48 DAT and a reduction in the assimilated K content at the end of the cycle (62 DAT). The maximum shoot dry mass production was 3.23 t ha−1 with a fertilization of 150 and 100 kg ha−1 of N and K, respectively. The average marketable fruit yield obtained in the two years of cultivation was 34.68 t ha−1, reaching a maximum value of 40.28 t ha−1 under 150 kg ha−1 N and K fertilization. Total soluble solids increased as a function of the increase in N Fert. and K Fert., with a maximum value of 8.07 °Brix. The models indicated that the K Fert. factor was decisive in the increase of this variable. In future studies it is necessary to increase the concentrations of the two factors in order to optimize the maximum viability of yield and °Brix.

Quantifying the organic carbon content of soil over large areas is essential for characterising the soil and the effects of its management. However, analytical methods can be laborious and costly. Reflectance spectroscopy is a well-established and widespread method for estimating the chemical-element content of soils. The aim of this study was to estimate the soil organic carbon (SOC) content using hyperspectral remote sensing. The data were from soils from two localities in the semi-arid region of Brazil. The spectral reflectance factors of the collected soil samples were recorded at wavelengths ranging from 350–2500 nm. Pre-processing techniques were employed, including normalisation, Savitzky–Golay smoothing and first-order derivative analysis. The data (n = 65) were examined both jointly and by soil class, and subdivided into calibration and validation to independently assess the performance of the linear methods. Two multivariate models were calibrated using the SOC content estimated in the laboratory by principal component regression (PCR) and partial least squares regression (PLSR). The study showed significant success in predicting the SOC with transformed and untransformed data, yielding acceptable-to-excellent predictions (with the performance-to-deviation ratio ranging from 1.40–3.38). In general, the spectral reflectance factors of the soils decreased with the increasing levels of SOC. PLSR was considered more robust than PCR, whose wavelengths from 354 to 380 nm, 1685, 1718, 1757, 1840, 1876, 1880, 2018, 2037, 2042, and 2057 nm showed outstanding absorption characteristics between the predicted models. The results found here are of significant practical value for estimating SOC in Neosols and Cambisols in the semi-arid region of Brazil using VIS-NIR-SWIR spectroscopy.

Detecting desertification in different years and rainfall regimes by 2D Scatter Plot