Carlos Antonio Da Silva Junior

Carlos Antonio Da Silva Junior
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Carlos Antonio verified their affiliation via an institutional email.
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Carlos Antonio verified their affiliation via an institutional email.
  • Dr.
  • Researcher at Mato Grosso State University

Professor at the State University of Mato Grosso (UNEMAT), Campus Sinop, Brazil

About

261
Publications
63,484
Reads
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3,468
Citations
Introduction
Adjunct Professor III at the State University of Mato Grosso (UNEMAT). Ad-hoc reviewer of national and international scientific journals such as Land Use Policy, Computers and Electronics in Agriculture, Nature Communications, Scientific Reports, and Scientific Data (Nature). He serves as Editor of the scientific journals, Remote Sensing Applications: Society and Environment (Elsevier), Frontiers in Climate (Frontiers), Big Earth Data (Taylor & Francis), Sustainability and Remote Sensing (MDPI).
Current institution
Mato Grosso State University
Current position
  • Researcher
Additional affiliations
February 2012 - December 2013
National Institute for Space Research
Position
  • Master Degree in Remote Sensing
February 2007 - December 2011
Mato Grosso do Sul State University
Position
  • Graduation in Agronomic Engineering
August 2015 - present
Mato Grosso State University
Position
  • Researcher
Description
  • http://dgp.cnpq.br/dgp/espelhogrupo/5228365125548035
Education
February 2013 - February 2016
February 2012 - December 2012
March 2007 - December 2011

Publications

Publications (261)
Article
Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in alm...
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Abstract Brazil is one of the world’s biggest emitters of greenhouse gases (GHGs). Fire foci across the country contributes to these emissions and compromises emission reduction targets pledged by Brazil under the Paris Agreement. In this paper, we quantify fire foci, burned areas, and carbon emissions in all Brazilian biomes (i.e., Amazon, Cerrado...
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Land cover mapping over large areas is essential to address a wide spectrum of socio-environmental challenges. For this reason, many global or regional land cover products are regularly released to the scientific community. Yet, the remote sensing community has not fully addressed the challenge to extract useful information from vast volumes of sat...
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The integration multispectral sensors with machine learning algorithms has demonstrated increasing efficacy in the classification of various maize morphophysiological characteristics. The hypothesis of this study is that maize plants subjected to different irrigation management practices exhibit distinct spectral behaviors, allowing for their class...
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Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of dema...
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The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspec...
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The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in the 2023/24 harvest in the experimental area of the Fed...
Article
The dynamics of carbon among atmospheric, soil and biotic stocks are of great importance for ecosystem and climate services. The interdependence of carbon stocks is volatile, since higher atmospheric CO₂ concentrations affect plant development and therefore carbon storage in terrestrial ecosystems. In addition, the carbon cycle is related to soil m...
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Identifying machine learning models that are capable of classifying soybean genotypes according to micronutrient content using only spectral data as input is relevant and useful for plant breeding programs and agricultural producers. Therefore, our objective was to classify soybean genotypes according to leaf micronutrient levels using multispectra...
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The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of th...
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Building models that allow phenotypic evaluation of complex agronomic traits in crops of global economic interest, such as grain yield (GY) in soybean and maize, is essential for improving the efficiency of breeding programs. In this sense, understanding the relationships between agronomic variables and those obtained by high-throughput phenotyping...
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Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species....
Article
This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022....
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Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon diox...
Article
This study aims to examine the seasonal spatial variability of column-averaged of dry air mole fraction of CO2 (XCO2) and Solar induced chlorophyll fluorescence (SIF) in the state of Mato Grosso in the Midwest region of Brazil, employing ordinary kriging (OK) as the spatial interpolation method. The XCO2 and SIF remote sensing data were collected o...
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Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybean...
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In recent decades, the main commercial crops of Mato Grosso, such as soybeans, corn, and cotton, have been undergoing transformations regarding the adoption of new technologies to increase production. However, regardless of the technological level, the climate of the region, including the rainfall regime, can influence the success of crops and faci...
Chapter
Este livro decorre de uma exitosa cooperação científica internacional no âmbito do acordo CAPES-COFECUB, programa bilateral de pesquisa Brasil-França, aprovado pela CAPES e COFECUB em 2018. O projeto intitulado CICLAMEN (cidades, clima e vegetação: modelagem e políticas públicas ambientais) reuniu durante cinco anos (de 2019 a 2023) pesquisadores e...
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Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional informat...
Article
The dynamics of land use and land cover (LULC) are of great importance for the management of natural resources, sustainable development and urban planning over geographic space, and this condition is sometimes supported by geoprocessing and remote sensing techniques. In addition, machine learning methods automate the classification and modeling of...
Article
This research aimed to evaluate the accuracy of machine learning techniques in distinguishing groups soybean genotypes according to grain industrial traits using hyperspectral reflectance of the leaves. A total of 32 soybean genotypes were evaluated and allocated in randomized blocks with four replications. At 60 days after emergence, spectral anal...
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A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure tra...
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Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In t...
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Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for fa...
Article
Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that t...
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Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discriminatio...
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The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing...
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Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate...
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The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP...
Article
Fast diagnostics from hyperspectral data and machine learning (ML) models to predict nitrogen (N) and pigment content in maize crops is challenging to optimize nitrogen fertilization. This research assessed the efficiency of the five ML algorithms, the best phenological stage, and the sensitivity of the 90 spectra to estimate N and pigment content....
Article
Understanding the variability of soil CO2 emission across several land use and cover (LULC) classes and biomes and its relationship with climate variables is important to drive strategies that contribute to meeting local and international demands for sustainable development and low carbon agriculture. The hypothesis of this research is that soil CO...
Article
Understanding the association between variables obtained by multispectral imaging, such as plant canopy reflectance at different wavelengths and vegetation indices (VIs), and agronomic traits of interest for soybean is crucial for breeding programs. This approach, associated with the evaluation of the best stage for spectral data collection, can id...
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Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input...
Article
The changes in landscapes have been followed more intensely in recent decades thanks to scientific advances, both in the field of technological improvement of satellites and in remote sensing techniques. Advanced and efficient machine learning techniques have helped remote sensing professionals to determine these changes, from the simplest to the m...
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The definition of the distance between sampling grid points directly impacts the development of fertility maps because it affects the spatial dependence of geostatistics and the estimates for locations not sampled in the interpolation. Based on geostatistical concepts, it is common to recommend one or more soil samples per hectare. However, there i...
Article
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The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of thi...
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Background: The Cerrado is the most biodiverse savanna and maintains other biomes. Aware of its significance, this paper evaluated the Brazilian Cerrado’s climatic, environmental, and socioeconomic aspects using remote sensing data and spatial statistics (correlation analysis and principal components analysis—PCA). Following the measures of sample...
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The dimensions of mechanized agricultural systems depend on the edaphoclimatic conditions, crops, and work regimes. This study aimed to geographically estimate the monthly available time and number of favorable hours for agricultural field spraying in the state of Mato Grosso do Sul, Brazil. The meteorological restrictions imposed during unfavorabl...
Article
Studies that focus on the concentration of methane and its relationship with fires in the Amazon have become relevant in the current scenario, especially due to the increasing environmental degradation associated with climate change. Therefore, the main objective of the study was to investigate the spatial–temporal variability in the observations o...
Article
The emission of soil carbon dioxide (CO2) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of s...
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High-throughput phenotyping (HTP) using vegetation indices (VIs) is an important data source for managing plant breeding programs and can be a promising tool in indirect selection. This study hypothesized that VIs are correlated with agronomic traits in corn, and therefore, HTP can be an auxiliary tool for selecting superior genotypes. The objectiv...
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Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more...
Chapter
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This chapter reviews key issues in using sensor data in precision agriculture (PA) and, in particular, their mode of deployment (proximal or remote). It assesses relative strengths and weaknesses of proximal sensing techniques, compared with imaging data typically acquired from remote sensing platforms, before assessing trade-offs in sensor data re...
Article
Soybean genotypes have distinct physicochemical characteristics, mainly regarding the oil and protein contents in the grains. The use of high-throughput phe-notyping technologies allied to data processing by machine learning algorithms facili-tates and can make it faster and more precise to obtain information about the charac-teristics of the grain...
Article
B deficiency is one of the most limiting factors for the growth of Eucalyptus spp. in Brazil, especially in the cerrado region, where there is the greatest expansion of forest production of the species. Based on multi or hyperspectral sensors and digital image processing techniques, the application in diagnosis is fast, nondestructive and local in...
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Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship w...
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Precipitation is crucial for the hydrological cycle and is directly related to many ecological processes. Historically, measurements of precipitation totals were made at weather stations, but spatial and temporal coverage suffered due to the lack of a robust network of weather stations and temporal gaps in observations. Several products have been p...
Article
The Guarani Aquifer System is one of the largest freshwater reservoirs in the world and has been widely exploited due to the quantity and quality of the stored water. The outcropping areas are considered highly vulnerable, and anthropic interference can influence the recharge potential and cause changes in water quality. The purpose of this researc...
Article
The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco2, ppm), and their interactions with climate and vegetation indices in eastern Amazo...
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The growth of the world population has led to the expansion of agricultural areas to produce food that meets world demand, making it necessary to increase productivity and maintain environmental sustainability in these areas. Seeking sustainable food production, the agricultural use of soil must be assessed in view of optimal use or land as natural...
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The Pantanal is the world’s largest and most biodiverse continental sheet-flow wetland. Recently, vast tracts of the Pantanal have succumbed to the occurrence of fires, raising serious concerns over the future integrity of the biodiversity and ecosystem services of this biome, including revenues from ecotourism. These wildfires degrade the baseline...
Article
The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quali...
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We analyzed the occurrences of fire foci between years 2001 and 2015 in Mato Grosso state, Brazil. For this, we used remote sensing data and we correlated fire with surface temperature , rainfall, wind speed, air temperature, relative humidity, and vegetation index. The data were analyzed within the state according to use and cover classes. We also...
Article
Spatiotemporal data fusion algorithms have been developed to fuse satellite imagery from sensors with different spatial and temporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn u...
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Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to...
Article
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Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in NEB. The objectives of this study are: (i) to evaluate the spatiotemporal e...
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In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon ob...
Article
The human influence on climate change has increased the occurrence of extreme events and made heat waves and droughts more frequent and severe, which leads to an increased number of fires. It is likely that in the past, the Atlantic Forest has responded to the occurrence of extreme events with fires that may have contributed to the reduction of for...
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Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome...
Article
Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study eva...
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Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazil...
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Corn has great relevance worldwide due to its economic and social importance. Its production grows every harvest, and determining traits directly related to the grain yield is essential for crop management. The hypothesis of this study is that structural equation modelling (SEM) and factor analysis allow us to identify agronomic variables and veget...
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This paper evaluates compliance with environmental laws in the municipality of Sorriso and the impact of changing legislation on vegetation. To verify the size of the properties, the areas designated as legal reserves (LRs), permanent protection areas (APPs), and springs were studied. Details of compliance with the New Forest Code (NFC) were drawn...
Article
Sampling trees in natural environment can be used in studies ranging from floristic composition and phytogeography to management and growth modelling, and accurate inventories are based on highly labor-intensive methods. Relying on hyperspectral approach, this study aimed to differentiate spectral libraries of four Amazon tree species. We first pre...
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Background Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength a...
Article
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In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface...
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Em 18 de fevereiro foi publicada na prestigiosa revista Die Erde ("A Terra", em alemão) a versão em inglês do seguinte texto sobre a rodovia BR-319 (Manaus-Porto Velho) (disponível aqui). Die Erde é publicada (sob diferentes nomes) pela Sociedade Geográfica de Berlim desde 1828, e é uma das revistas profissionais mais antigas do mundo. Leia a segui...
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The Brazilian government intends to complete the paving of the BR-319 highway, which connects Porto Velho in the deforestation arc region with Manaus in the middle of the Amazon Forest. This paving is being planned despite environmental legislation, and there is concern that its effectiveness will cause additional deforestation, threatening large p...
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The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019...
Article
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In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine...
Article
This study evaluated the influence of the urban expansion in the socioeconomic, demographic, and environmental indicators in the City of Arapiraca, Brazil, based on orbital products and multivariate analysis. It was used the remote data (Land Use and Land Cover-LULC, Normalized Difference Vegetation Index, and Land Surface Temperature) between 1985...
Article
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The rainfall is essential to Brazil's hydrological cycle, agricultural development, and power generation, mainly Cerrado biome. Thus, the study assessed the influence of the El Niño–Southern Oscillation (ENSO) and synoptic systems on rainfall variability over the Brazilian Cerrado. To evaluate this variability, it used monthly rainfall data from th...
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The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforesta...
Article
Fire is used in the management of pastures, renewal and expansion of areas, and agricultural activities in South America (SA). The objectives of this study were: i) to identify the countries and regions with the highest number of fire foci in SA, and ii) to evaluate the spatial dynamics of fire foci based on the Meteorological Fire Danger Index (MF...
Article
One of the main objectives of soybean breeding programs is the search for genotypes that are both high yielding and tolerant to abiotic stresses. Brazilian Cerrado, the main grain‐producing region in the country, is characterised by naturally acidic and low fertility soils that consequently have low base saturation. Therefore, identifying genotypes...
Article
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The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environm...
Article
In decades, Brazilian cities have undergone profound changes, accentuated after implementing public policies such as Programa de Aceleração do Crescimento (PAC) and Minha Casa Minha Vida (PMCMV). This article assessed the urban expansion in the Benedito Bentes (BB) district, Maceió, Brazil, during 1987-2017. The objectives of this investigation wer...
Article
Growth and production models can help to simulate the growth of tree dimensions to predict forest productivity at different levels. In this context, the following questions arise: (i) is it possible to recognize the growth pattern of eucalyptus species based on spectral features using machine learning (ML) for data modeling? (ii) what spectral feat...
Chapter
Full-text available
Os sensores imageadores hiperespectrais realizam a aquisição das imagens em centenas de bandas estreitas e continuas, fazendo com que cada pixel da imagem derive uma curva de reflectância espectral tendo, assim, alto potencial e flexibilidade em fornecer informações detalhadas de um alvo ou objeto. Pensando-se na potencialidade do uso dessas imagen...
Article
Boron (B) is an essential element whose deficiency results in rapid inhibition in the growth of plants, acting on their meristematic growth. Real-time monitoring of B fertilization in eucalyptus is helpful for guiding precision diagnosis and efficient management of plant boron nutrition. This research hypothesizes that different boron levels alter...
Article
The search for high-yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and ad...
Article
Full-text available
In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aer...
Article
The Brazilian Legal Amazon is an extensive territory (5,088,668.25 km2) in which different factors (environmental and social) influence the fire dynamics of the region. This study aims to explain the seasonal patterns of meteorological variables, fire, land use, and carbon emissions and their inter-relationships, focusing on years of El Niño–Southe...
Article
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This study aims to characterize the wind regime in the state of Rio de Janeiro (SRJ), Brazil, and relate it to the physiographic aspects and influence of meteorological systems, based on 12 Brazilian National Institute of Meteorology (INMET) automatic meteorological stations, for the period from 2008 to 2019. Box plots are used for the assessment o...
Article
The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios (ARIMA time-series model number). The study was carrie...
Article
Carrying out monitoring during the crop cycle through vegetation indices (VIs) with obtained unmanned aerial vehicle allows agility in decisions about management practices, as well as concerning nutritional deficiencies in crops, as nitrogen (N). This nutrient absorbed in greater quantity, and that most influences the grain yield in corn. This rese...

Questions

Question (1)
Question
I am working with hyperspectral airborne data and would like to know if anyone could help me to do this kind of correlation chart between spectral bands? What software to perform? What would the procedure look like? Thank you.

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