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This paper presents the observational, remote sensing, and model simulation used to analyze southern Brazil Antarctic ozone hole influence (SBAOHI) events that occurred between 2005 and 2014. To analyze it, we use total ozone column (TOC) data provided by a Brewer spectrophotometer (BS) and the OMI (Ozone Monitoring Instrument). In addition to the AURA/MLS (Microwave Limb Sounder) instrument, satellite ozone profiles were utilized with DYBAL (Dynamical Barrier Localization) code in the MIMOSA (Modélisation Isentrope du Transport Mésoéchelle de l'Ozone Stratosphérique par Advection) model Potential Vorticity (PV) fields. TOC has 7.0 ± 2.9 DU reductions average in 62 events. October has more events (30.7%). Polar tongue events are 19.3% in total, being more frequently observed in October (50% of cases), with medium intensity (58.2%), and in the stratosphere medium levels (55.0%). Already, polar filament events (80.7%) are more frequent in September (32.0%), with medium intensity (42.0%), and stratosphere medium levels (40.7%). [ABSTRACT FROM AUTHOR]

Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers' decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, and the inherent complexity and inexplicability of these models hinder their widespread adoption. This paper presents a simpler approach, employing linear regression models fitted from vegetation indices (VIs) extracted from MODIS sensor data on the Terra and Aqua satellites. The aim is to forecast cotton yields in key areas of the Brazilian Cerrado. Using data from 281 commercial production plots, models were trained (167 plots) and tested (114 plots), relating seed cotton yield to nine commonly used VIs averaged over 15-day intervals. Among the evaluated VIs, Enhanced Vegetation Index (EVI) and Triangular Vegetation Index (TVI) exhibited the lowest root mean square errors (RMSE) and the highest determination coefficients (R2). Optimal periods for in-season yield prediction fell between 90 and 105 to 135 and 150 days after sowing (DAS), corresponding to key phenological phases such as boll development, open boll, and fiber maturation, with the lowest RMSE of about 750 kg ha−1 and R2 of 0.70. The best forecasts for early crop stages were provided by models at the peaks (maximum value of the VI time series) for EVI and TVI, which occurred around 80–90 DAS. The proposed approach makes the yield predictability more inferable along the crop time series just by providing sowing dates, contour maps, and their respective VIs. [ABSTRACT FROM AUTHOR]

The Tocantins–Araguaia Basin covers an extensive area of Brazilian territory and has a water reservoir system installed in a cascade that is very important for water storage and hydropower production. There is concern about the use of this resource because of the current tendency of surface water reduction in the reservoir systems. Sustainable management can help to guarantee the water supply and the hydropower supply. However, accurate data on the monitoring parameters are required. This study aims to analyze which factors most influence the changes in the areas of water reservoirs and, subsequently, the changes in the water availability. Trends, correlations, and principal component analyses (PCAs) were used to assess the association between the areas of water reservoirs and the liquid evaporation, discharge, precipitation, and land use and land cover (LULC) in the basin. We observed that the precipitation did not decrease significantly. On the other hand, changes in the LULC and the areas of reservoirs were highly correlated with each other. The PCA also showed a strong association between meteorological factors and the areas of reservoirs. In conclusion, this study showed that reductions in the areas of water reservoirs are mostly related to deforestation and reduced reservoir discharge rather than climate change. This paper provides a straightforward approach to monitoring reservoir changes based on the tendencies of some parameters that are strongly correlated with reservoir changes. [ABSTRACT FROM AUTHOR]

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product. [ABSTRACT FROM AUTHOR]

The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil's Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions. [ABSTRACT FROM AUTHOR]

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 in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region's area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin's concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions. [ABSTRACT FROM AUTHOR]

The EOSOLAR project was designed to investigate the structure of the atmospheric boundary layer in an equatorial coastal zone, where the discontinuity of surface conditions induces non-stationarity gradients of wind speeds and the development of internal boundary layers. The proposed methodology considers several aspects of the sea–land transition meteorology that are essential for precisely estimating wind–solar energy potential and assessment of structural loads on wind turbines. Infrared (LIDAR) and acoustic (SODAR) ground-based remote sensing instruments and micrometeorological towers were installed in a near-shore equatorial area of northeast Brazil, in order to provide a comprehensive view of meteorological processes. This paper reports a description of the project study area, methodology, and instrumentation used. Details of instruments configurations, a validation of micrometeorology towers, and a comparison between the LIDAR and SODAR are presented. Results of the first field campaign measuring the coastal flow, integrating the micrometeorological tower and LIDAR observations are described. [ABSTRACT FROM AUTHOR]

The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome. [ABSTRACT FROM AUTHOR]

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 information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a valuable tool for measuring and estimating ET, particularly in the data-scarce Amazon region. A scientometric analysis was conducted to identify the most used RS-based ET product or model in Brazil and its potential application in the Amazon. Scientometrics allows for the quantitative analysis of scientific output; this study identified the most widely used RS product in the Amazon biome. Articles published in Web of Science, Scielo, and Scopus databases up to 2022 were searched using the keywords "Evapotranspiration", "Remote Sensing", and "Brazil". After initial screening, 140 relevant articles were subjected to scientometric analysis using the Bibliometrix library in RStudio 2023.06.1+524. These articles, published between 2001 and 2022, reveal a collaborative research landscape involving 600 authors and co-authors from 245 institutions, with most studies originating from Brazil's Southeast and North (Amazon) regions. Notably, within the 12 studies focusing on ET by RS in the Amazon biome, applications were diverse, encompassing river basins, climate change, El Niño, and deforestation, with the MOD16 product being the most frequently employed. [ABSTRACT FROM AUTHOR]

In the last decades, several methodologies for estimating crop phenology based on remote sensing data have been developed and used to create different algorithms. Although many studies have been conducted to evaluate the different methodologies, a comprehensive understanding of the potential of the different current algorithms to detect changes in the growing season is still lacking, especially in large regions and with more than one crop per season. Therefore, this work aimed to evaluate different phenological metrics extraction methodologies. Using data from over 1500 fields distributed across Brazil's central area, six algorithms, including CropPhenology, Digital Earth Australia tools package (DEA), greenbrown, phenex, phenofit, and TIMESAT, to extract soybean crop phenology were applied. To understand how robust the algorithms are to different input sources, the NDVI and EVI2 time series derived from MODIS products (MOD13Q1 and MOD09Q1) and from Sentinel-2 satellites were used to estimate the sowing date (SD) and harvest date (HD) in each field. The algorithms produced significantly different phenological date estimates, with Spearman's R ranging between 0.26 and 0.82 when comparing sowing and harvesting dates. The best estimates were obtained using TIMESAT and phenex for SD and HD, respectively, with R greater than 0.7 and RMSE of 16–17 days. The DEA tools and greenbrown packages showed higher sensitivity when using different data sources. Double cropping is an added challenge, with no method adequately identifying it. [ABSTRACT FROM AUTHOR]

Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the decades, a variety of mapping approaches have been employed for the detection of landslides using Earth observation (EO) data. Object-based image analysis (OBIA) is a widely recognized method for mapping landslides and other morphological features. In Brazil, despite the high frequency of landslides, methods for inventory construction are poorly developed. The aim of this study is to semi-automatically recognize shallow landslides in Itaóca (Brazil) and evaluate the transferability of the approach within different areas in Brazil. RapidEye satellite images (5 m) and the derived normalized difference vegetation index (NDVI), as well as a digital elevation model (DEM) (12.5 m) and morphological data, were integrated into the classification. The results show that the method is suitable for the recognition of this type of hazard in Brazil. The overall accuracy was 89%. The main challenges were the identification of small landslides and the exact delineation of scars. The findings validate the applicability of the approach in Brazil, although additional adjustments to the primary rule set might lead to better results. [ABSTRACT FROM AUTHOR]

Biomass burning (BB) emissions negatively impact the biosphere and human lives. Orbital remote sensing and modelling are used to estimate BB emissions on regional to global scales, but these estimates are subject to errors related to the parameters, data, and methods available. For example, emission factors (mass emitted by species during BB per mass of dry matter burned) are based on land use and land cover (LULC) classifications that vary considerably across products. In this work, we evaluate how BB emissions vary in the PREP-CHEM-SRC emission estimator tool (version 1.8.3) when it is run with original LULC data from MDC12Q1 (collection 5.1) and newer LULC data from MapBiomas (collection 6.0). We compare the results using both datasets in the Brazilian Amazon and Cerrado biomes during the 2002–2020 time series. A major reallocation of emissions occurs within Brazil when using the MapBiomas product, with emissions decreasing by 788 Gg (−1.91% year−1) in the Amazon and emissions increasing by 371 Gg (2.44% year−1) in the Cerrado. The differences identified are mostly associated with the better capture of the deforestation process in the Amazon and forest formations in Northern Cerrado with the MapBiomas product, as emissions in forest-related LULCs decreased by 5260 Gg in the Amazon biome and increased by 1676 Gg in the Cerrado biome. This is an important improvement to PREP-CHEM-SRC, which could be considered the tool to build South America's official BB emission inventory and to provide a basis for setting emission reduction targets and assessing the effectiveness of mitigation strategies. [ABSTRACT FROM AUTHOR]

In this paper, the authors use remote-sensing images to monitor the water quality of reservoirs located in the semiarid region of Northeast Brazil. Sentinel-2 MSI TOA Level 1C reflectance images were used to remotely estimate the concentration of chlorophyll-a (chl-a), the main indicator of the trophic state of aquatic environments, in five reservoirs in the state of Ceará, Brazil. A three-spectral band retrieval model was calibrated using 171 water samples, collected from November 2015 through July 2018 in 5 reservoirs. For validation, 71 additional samples, collected from August 2018 through December 2019, were used to ensure a robust accuracy assessment. The TOA Level 1C products performed very well, achieving a relative RMSE of 28% and R2 = 0.80. Data on wind direction and speed, solar radiation and reservoir volume were used to generate a conceptual model to analyze the behavior of chl-a in the surface waters of the Castanhão reservoir. During 2019, the reservoir water quality showed strong variation, with concentration fluctuating from 30 to 95 µg/L We showed that the end of the dry season is marked by strong eutrophic conditions corresponding to very low water inflows into the reservoir. During the rainy season there is a large decrease in the chl-a concentration following the increase of the lake water storage. During the following dry season, satellite data show a progressive improvement of the trophic state controlled by wind intensity that promotes a better mixing of the reservoir waters and inhibiting the development of most phytoplankton. [ABSTRACT FROM AUTHOR]

Remote sensing is valuable for estimating aboveground biomass (AGB) stocks. However, its application in agricultural and pasture areas is limited compared with forest areas. This study quantifies AGB in agriculture–pasture mosaics within Brazil's Campo Maior Complex (CMC). The methodology employs remote sensing cloud processing and utilizes an estimator to incorporate vegetation indices. The results reveal significant changes in biomass values among land use and land cover classes over the past ten years, with notable variations observed in forest plantation, pasture, sugar cane, and soybean areas. The estimated AGB values range from 0 to 20 Mg.ha−1 (minimum), 53 to 419 Mg.ha−1 (maximum), and 19 to 57 Mg.ha−1 (mean). In Forest formation areas, AGB values range from approximately 0 to 278 Mg.ha−1, with an average annual value of 56.44 Mg.ha−1. This study provides valuable insights for rural landowners and government officials in managing the semiarid territory and environment. It aids in decision making regarding agricultural management, irrigation and fertilization practices, agricultural productivity, land use and land cover changes, biodiversity loss, soil degradation, conservation strategies, the identification of priority areas for environmental restoration, and the optimization of resource utilization. [ABSTRACT FROM AUTHOR]

An extension of the linear H ∞ filter, presented here as the extended H ∞ particle filter (E H ∞ PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H ∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H ∞ particle filter (E H ∞ PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The E H ∞ PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the E H ∞ PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles. [ABSTRACT FROM AUTHOR]

Fire events are increasing in frequency, duration, and severity worldwide. The combination of ash and uncovered land might increase the transportation of pollutants into the streams, potentially affecting the water supply systems. The intensifying fires in Brazil's Cerrado biome, responsible for 70% of the country's water supply, give rise to profound ecological, climatic, and socio-economic concerns that require urgent and effective mitigation strategies. However, little attention has been paid to the consequences of fire events on water resources in the region. In this study, the Fire Impact on Water Resources Index (FIWRI) is proposed and applied in six different water supply watersheds to analyse fire behaviour from 2003 to 2020 and its potential impact on inland water bodies. This is the first remote-sensing-based index for fire impact on water resources developed for the Brazilian territory, to support water management on a watershed scale and uses variables such as terrain slope, river proximity, and vegetation to classify fire events as having a low to high potential to contaminate water bodies. We observed that all six water supply watersheds suffered frequent fire events, with different FIWRI proportions, which ranged from High to Low FIWRI. The proposed index could be used in real-time fire monitoring alert systems in order to support water supply management. [ABSTRACT FROM AUTHOR]

Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps' attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation. [ABSTRACT FROM AUTHOR]

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2). [ABSTRACT FROM AUTHOR]

Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants. [ABSTRACT FROM AUTHOR]

Two lunar flashes are reported and fully analyzed, with one of them fulfilling every criterion preconized in the literature for the characterization of an impact, including confirmation by two simultaneous observations. It happened at 07:13:46 UT on 14 December 2017, at the selenographic coordinates of 9.79° (±0.06°)N and 45.42 (±0.07°)E. The peak magnitudes in the R and V bands vary from 6.3 to 7.9 and from 7.4 to 9.0, respectively, depending on the observatory, as the cameras' exposure times were considerably different. The impactor mass is estimated to be between 1.6 and 2.0 kg, with a diameter of 10 to 11 cm, having produced a crater of 8.4 to 8.9 m in diameter. Results for the second flash are also presented and discussed, although the confirmation of an impact was not possible due to a pause in the recordings at one of the sites. The observations took place as part of an inaugural observing campaign in Brazil for lunar impact flash (LIF) detection conceived by the Brazilian Meteor Observation Network (BRAMON) and were carried out by two teams located in different states in the Northeast Region of Brazil, about 353 km apart from each other, at a time when the Moon was crossing the densest part of the Geminid meteoroid stream in 2017. The observing setups included 0.13 m and 0.2 m telescopes, both equipped with sensitive cameras. The Maceió setup probably delivered the finest definition ever reported in the literature for lunar impact monitoring, resulting in high-accuracy positioning. This will certainly aid in finding the associated crater from orbiter images, which will substantiate another work, aimed at performing a comparative analysis between the results from our photometry and the data retrieved by the LRO images. These observations were also very likely the first and the only one so far made by a normal framerate camera and a long-exposure camera simultaneously. The associated benefits are commented on. The source of the impactors is also discussed. In view of the successful results of this experience, national observing campaigns of this kind will be given continuation. [ABSTRACT FROM AUTHOR]

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome. [ABSTRACT FROM AUTHOR]

Due to different combinations of agriculture, livestock and forestry managed by rotation, succession and intercropping practices, integrated agriculture production systems such as integrated crop–livestock systems (iCL) constitute a very complex target and a challenge for automatic mapping of cropping practices based on remote sensing data. The overall objective of this study was to develop a classification strategy for the annual mapping of integrated Crop–Livestock systems (iCL) at a regional scale. This strategy was designed and tested in the six agro-climatic regions of Mato Grosso, the largest Brazilian soybean producer state, using MODIS satellite time-series images acquired between 2012 and 2019, ground data with heterogeneous distribution in space and time and a Random Forest classifier. The results showed that: 1. the use of unbalanced training samples with a class composition close to the real one was the right classifier training strategy; 2. the use of a single training database (pooling samples from different years and regions) to classify each region and year individually proved to be robust enough to provide similar classification accuracies in comparison to those based on the use of a database acquired for each region and for each year. The final hierarchical classification overall accuracy was 0.89 for Level 1, the cropping pattern level (single and double crops DC); 0.84 for Level 2, the DC category level (integrated system iCL soy-pasture/brachiaria, soy-cotton and soy-cereal); 0.77 for Level 3, the iCL level (iCL1 soy-pasture and iCL2 soy-pasture mixed with corn). The F-scores for DC, iCL and iCL1 cropping systems presented high accuracy (0.89, 0.85 and 0.84), while iCL2 was more difficult to classify (0.63). This approach will next be applied across the entire Brazilian soybean corridor, leading to an operational tool for monitoring the adoption of sustainable intensification practices recognized by Brazil's Agriculture Low Carbon Plan (ABC PLAN). [ABSTRACT FROM AUTHOR]

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil and West Africa, leading to significant environmental and socio-economic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA's 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus's 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state. [ABSTRACT FROM AUTHOR]

Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions. [ABSTRACT FROM AUTHOR]

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country's total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and the NEB rate (0.5). In this sense, the present work analyzes the microphysical characteristics of clouds with and without the occurrence of total lightning. For this purpose, data from the Lightning Imaging Sensor (LIS), TRMM Microwave Imager (TMI) and Precipitation Radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2013 were used. The TRMM data were analyzed to establish a relationship between the occurrence of lightning and the clouds' microphysical characteristics, comparing them as a function of lightning occurrence classes, spatial location and atmospheric profiles. A higher lightning occurrence is associated with higher values of ice water path (>38.9 kg m−2), rain water path (>2 kg m−2), convective precipitation (>5 mm h−1) and surface precipitation (>7 mm h−1), in addition to slightly higher freezing level height values. Reflectivity observations (>36 dBZ) demonstrated typical convective profile curves, with higher values associated with classes with higher lightning densities (class with more than 6.8 flash km−2 year−1). [ABSTRACT FROM AUTHOR]

Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to evaluate CHIRPS version 2, MSWEP version 2, and PERSIANN-CDR, compared to gridBR, as daily mean and extreme inputs represented on a monthly scale and their respective seasonal trends of rainfall in the Mearim River Drainage Basin (MDB), Maranhão state, Brazil. Estimates of errors were calculated (relative error, pbias; root mean square error, RMSE, and Willmott concordance index, d), and the chances of precipitation were estimated by remote sensing (RES). In addition, trends in precipitation were estimated by the two-sample Mann–Kendall test. Given the overall performance, the best products for estimating monthly mean daily rainfall in the MDB are CHIRPS and PERSIANN-CDR, especially for rainy months (December to May). For daily extremes on the monthly scale, the best RES is PERSIANN-CDR. There is no general agreement between gridBR and RES methods for the trend signal, even a nonsignificant one, much less a significant one. The use of MSWEP for the MDB region is discouraged by this study because it overestimates monthly averages and extremes. Finally, studies of this kind in drainage basins are essential to improve the information generated for managing territories and developing regionalized climate and hydrological models. [ABSTRACT FROM AUTHOR]