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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 facilitate, or not, the maximum production efficiency. This study aimed to define the behavior of the variability in monthly and annual rainfall and its probability of monthly occurrence and calculate the drought index for the northwestern region of Mato Grosso, in the southern region of the Brazilian Amazon. To carry out the study, daily rainfall records were collected, calculating the totals for each month of the historical series for each of the four National Water and Sanitation Agency (ANA) rain gauge stations, Aripuanã (1985–2020), Colniza (2001–2020), Cotriguaçu (2004–2020), and Juína (1985–2020), representing the northwestern region. The annual distribution of rainfall during the periods studied ranged from 1376.2 to 3017.3 mm. The monthly distribution indicated a typical water shortage in the months of June, July, and August. The probability of rainfall near the average for each month was more than 50%. The monthly SPI-1 index revealed a total of 56 months affected by very dry events and 34 extreme dry events. The annual SPI-12 index pointed to seven very dry years and five extremely dry years. Therefore, the region presented high rainfall rates in most years; however, a significant process of drought was also observed, including in rainy months, which are the periods with the greatest demand for the main agricultural crops. [ABSTRACT FROM AUTHOR]

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 this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes. [ABSTRACT FROM AUTHOR]

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 the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms. [ABSTRACT FROM AUTHOR]

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 this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [ABSTRACT FROM AUTHOR]

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 with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp. [ABSTRACT FROM AUTHOR]

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 of natural ecosystems and the ecotourism economy across the region. Local residents ("Pantaneiros") anecdotally state that extensive cattle herbivory can solve the contemporary flammability problem of the Pantanal by controlling vegetation biomass, thereby preventing or reducing both fuel loads and fires across the region. Here, we examine the covariation between the presence and density of cattle and the incidence of fires across the Brazilian Pantanal. Variables assessed included bovine cattle density, SPI (Standardized Precipitation Index), GPP (Gross Primary Productivity)/biomass estimate, and fire foci along a 19-year time series (2001 to 2019). Our findings show that fire foci across the Pantanal biome are related to climatic variables, such as lower annual precipitation and higher annual drought indices (SPI) rather than to cattle stocking rates. Therefore, the notion of "cattle firefighting", a popular concept often discussed in some academic circles, cannot be validated because cattle numbers are unrelated to aboveground phytomass. Gross primary productivity further invalidated the "cattle herbivory" hypothesis because GPP was found to be strongly correlated with cattle density but not with the spatial distribution of fires. Fires throughout the Pantanal are currently aggravated by the presence of livestock and result from a combination of extreme weather events and outdated agricultural practices. [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 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 carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS, both in Brazil. A time series of 19 years was performed with data acquired by remote sensing from the following satellites: i) Landsat-8 (OLI and TIRS), and ii) TERRA/AQUA (MODIS). The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time, with a satisfactory correlation in future predictions of the variables for the next ten years, in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality, and CO2Flux tends to decrease in its seasonality, indicating a higher CO2 absorption. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the annual standardized precipitation index (SPI) obtained from the DrinC software based on multivariate analysis in the identification of rainfall and drought extremes in the State of Alagoas and its relationship with El Niño–Southern Oscillation. Monthly rainfall data from 1960 to 2016 from National Water Agency were analysed. Annual SPI (SPI‐12) has been designed for comparison with ENSO phases via Oceanic Niño Index for 3.4 region and in identifying climate extremes in the State of Alagoas. The principal component analysis and cluster analysis techniques were applied to the rainfall series of SPI‐12. Extreme events were identified in both rainy and drought periods according to SPI‐12, and were associated with the ENSO phases (El Niño, La Niña, and Neutral). The first four principal components explained 46.68% of the variance. Our findings are crucial for agriculture and civil defence since northeastern Brazil has several areas of risk and social vulnerability. [ABSTRACT FROM AUTHOR]

This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale. [ABSTRACT FROM AUTHOR]

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New techniques and improvements are required to quantify soil’s chemical and physical properties on production environment, reducing environmental impacts and minimizing soil analysis time. The aim of this study is to evaluate the possibility to estimate the content of silt, sand, clay, total iron and organic matter in soils formed by different lithologies in Parana State, Brazil, using VIS-NIR spectrum associated with Partial Least Square Regression (PLSR). 200 soil samples were collected in an area formed by Lixisols, Cambisols, Ferralsols, Arenosols and Nitisols in a depths of 0-0.2 and 0.2-0.8 m. Spectral readings were obtained in laboratory by FieldSpec 3 JR sensor. The spectral curves of the samples were correlated to the attributes through PLSR. The results obtained for sand in prediction were better when compared to the other attributes, presenting R2 = 0.90, r = 0.95 and RPD = 2.3. Clay and total iron presented satisfactory results, mainly for RPD values, which were above 2.4. Based on the results, it can be concluded that the PLSR technique associated with the spectral response of the soils, was able to estimate sand, clay and total iron with accuracy in a region formed by reworked materials, derived from several lithologies. [ABSTRACT FROM AUTHOR]

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 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin. [ABSTRACT FROM AUTHOR]

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 portions of forest, conservation units (CUs), and indigenous lands (ILs) in the surrounding areas. In this study, we evaluated environmental degradation along the BR-319 highway from 2008 to 2020 and verified whether highway maintenance has contributed to deforestation. For this purpose, we created a 20 km buffer adjacent to the BR-319 highway and evaluated variables extracted from remote sensing information between 2008 and 2020. Fire foci, burned areas, and rainfall data were used to calculate a drought index using statistical tests for a time series. Furthermore, these were related to data on deforestation, CUs, and ILs using principal component analysis and Pearson's correlation. Our results showed that 743 km2 of forest was deforested during the period evaluated, most of which occurred in the last four years. A total of 16,472 fire foci were identified. Both deforestation and fire foci occurred mainly outside the CUs and ILs. The most affected areas were close to capital cities, and after resuming road maintenance in 2015, deforestation increased outside the capital cities. Current government policy for Amazon occupation promotes deforestation and will compromise Brazil's climate goals of reducing greenhouse gas (GHG) emissions and deforestation. [ABSTRACT FROM AUTHOR]

• Real-time monitoring of boron fertilization in eucalyptus is helpful for guiding precision diagnosis; • The 350–371 nm spectral range can be used for detecting boron-deficient plants; • Adequate boron levels can be identified by using the 426–444, 1811–1910, 1948–2115, and 2124–2208 nm; • The 425–475 nm spectral range can be used to find boron-toxicity plants. 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 the reflectance of different wavelengths in eucalyptus. In this context, the objective of this study was to identify spectral ranges that can be used to monitor the boron status in eucalyptus plants. The experiment was carried out in a greenhouse, in which the treatments consisted of increasing boron levels in the form of boric acid (17% of B), whose levels varied from deficit to toxicity. Thus, five treatments were established: no boron, 1, 10, 20, and 40 mg/dm3 of boron. The remote sensing data used were bands, heights, and vegetation indices calculated after obtaining the spectral curves in each treatment. Our findings show that it is possible to accurately distinguish the boron levels in eucalyptus using hyper and multispectral bands. The 350–371 nm spectral range can be used for detecting boron-deficient plants. Plants with adequate boron levels can be identified by using the 426–444 nm, 1811–1910 nm, 1948–2115 nm, and 2124–2208 nm spectral ranges. Finally, the 425–475 nm spectral range can be used to find boron-toxicity plants. [ABSTRACT FROM AUTHOR]

Wind damage is one of the major factors affecting forest ecosystem sustainability, especially in the coastal region. Typhoon Lekima is among the top five most devastating typhoons in China and caused economic losses totaling over USD 8 billion in Zhejiang Province alone during 9–12 August 2019. However, there still is no assessment of its impacts on forests. Here we detected forest damage and its spatial distribution caused by Typhoon Lekima by classifying Landsat 8 OLI images using the random forest (RF) machine learning algorithm and the univariate image differencing (UID) method on the Google Earth Engine (GEE) platform. The accuracy assessment indicated a high overall accuracy (>87%) and kappa coefficient (>0.75) for forest-damage detection, as evaluated against field-investigated plot data, with better performance using the RF method. The total affected forest area by Lekima was 4598.87 km2, accounting for 8.44% of the total forest area in Zhejiang Province. The light-, moderate- and severe-damage forest areas were 2106.29 km2, 2024.26 km2 and 469.76 km2, respectively. Considering the damage severity, the net forest canopy loss fraction was 2.57%. The affected forest area and damage severity exhibited large spatial variations, which were affected by elevation, slope, precipitation and forest type. Our study indicated a larger uncertainty for affected forest area and a smaller uncertainty for the proportion of damage severity, based on multiple assessment approaches. This is among the first studies on forest damage due to typhoons at a regional scale in China, and the methods can be extended to examine the impacts of other super-strong typhoons on forests. Our study results on damage severity, spatial distribution and controlling factors could help local governments, the forest sector and forest landowners make decision on tree-planting planning and sustainable management after typhoon strikes and could also raise public and governmental awareness of typhoons' damage on China's inland forests. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify 'red tide' events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis 'red tide' bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8). [ABSTRACT FROM AUTHOR]

Cotton has high production costs compared to other annual crops because large numbers of plant protection product (PPP) applications can be needed to control insect pests, diseases, and growth. The hypothesis underlying this study was that vegetation indices (VIs) could be used to estimate application rates for cotton. Our objectives were to (i) evaluate the relationship between different VIs and the application rates for cotton; (ii) propose a modification to the canopy chlorophyll content index (CCCI); and (iii) to develop a VI based equation that will indicate the ideal application rate needed to maximize deposition in the middle layer of a cotton crop. The experiments were carried out during the crop seasons 2017/18, and 2018/19 in the State of Mato Grosso do Sul, Brazil. A multispectral sensor installed in an unmanned aerial vehicle (UAV) was used to obtain the VIs, and the application rates evaluated were 40, 70, 100, and 130 L ha−1. The spray deposits on cotton leaves were measured using the mass balance analysis method. Our findings revealed that an increase in the VIs led to a rise in the application rate needed to maintain spray deposition on the middle layer of cotton plants. The CCCI is related to the rate variation in the cotton crop. However, our results showed that the proposed modified equation (the simplified modified canopy chlorophyll content index), which is based on the relative deposition, improves the estimation of the application rate that will optimize spray deposition in the middle layer of cotton plants. [ABSTRACT FROM AUTHOR]

Brazil has the largest commercial beef cattle stock on Earth, and most of the cattle produced in the country is bred and finished on pastures. The cattle ranching sector represents a significant source of the country's greenhouse gas (GHG) emissions. Agricultural intensification has been highlighted as one of the main strategies in reaching global food security and reducing deforestation. The Sudden Death Disease (SDD) of pastures, which affects the most planted cultivar of Urochloa brizantha , is degrading pastures in the Amazon, contributing to low production yields and high emission rates. This paper discusses the intensification of pasture production systems and SDD, to examine the potential for pasture renovation to address livestock productivity and GHG balance, emissions and potential sinks. Does SDD represent a blessing or a curse to climate change mitigation in the Brazilian Amazon? A collection of pasture samples were assessed to measure wet and dry weight in areas with and without SDD, which were related to remote sensing data to provide an overall estimate of the total area affected by the SDD in Alta Floresta, a municipal county of southern Brazilian Amazonia. We found that 77.1% of all pastures had been committed to the syndrome, which has forced farmers to renew their pastures. This also has great potential in increasing soil carbon stocks, effectively reducing the CO 2 footprint of meat production in those areas. Therefore, we firmly believe that SDD management has provided an opportunity to rebalance the emissions/sequestration equation associated with meat production by the cattle ranching sector in this Amazonin frontier. • Livestock is the main driver of deforestation in the Brazilian Amazon. • The Sudden Death Disease (SDD) affects Brazilian pastures. • SDD has promoted the overall reduction in the greenhouse gas emissions. • Digital images of orbital sensors are able to detect pasture problems. [ABSTRACT FROM AUTHOR]

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 (Xco 2 , ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO 2 sources and sinks. Temporal changes indicate atmospheric Xco 2 in the range from 362.2 to 403.4 ppm. Higher Xco 2 values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m−2 sr−1 μm−1 explained the temporal changes of Xco 2 in 58% (R2 adj = 0.58; p < 0.001) and precipitation in 27% (R2 adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco 2 (ppm) anomalies were 6.19 ppm (source) and −6.29 ppm (sink), respectively. We conclude that the hot moments of Xco 2 in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco 2 spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest. • The hot moments of Xco 2 in the eastern Amazon rainforest are concentrated in the dry season of the year. • Xco 2 spatial hot spots and anomalies are concentrated in the southern region. • Xco 2 spatial hot spots and anomalies close to protected areas of the Amazon rainforest. • Xco 2 hot spots for the eastern Amazon in the Maranhão State, Brazil, are concentrated from May to September. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), 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 evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]

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 (MFDI) and future scenarios through numerical simulations. Fire foci time series comprised 21 years (1998–2018) from the BDQueimadas database. Cluster Analysis (CA), descriptive and exploratory statistics were employed. Fire foci maps for SA were made in 10-km pixel dimensions. MFDI was used to assess fire danger via SPEEDY (Simplified Parametrizations, primitivE-Equation DYnamics) model simulations. Three simulations were performed: control scenario (1980–2015), RCP2.6 scenario (optimistic - 2016 and 2050), and RCP8.5 scenario (pessimistic - 2015 and 2050). Regionally, three homogeneous groups of fire foci (G1, G2 and G3) and one atypical (NA - Not Grouped) were identified for Brazil via CA. The highest fire foci occurred in Brazil (62.72%), followed by Bolivia (9.03%), Argentina (8.28%), Venezuela (6.11%), Paraguay (5.94%), and Colombia (3.87%), respectively. The highest density of fire foci occurred in the MATOPIBA region, the confluence of Maranhão, Tocantins, Piauí, and Bahia, - (agricultural frontier), and also in the Cerrado-Amazon transition and the states of Mato Grosso and Mato Grosso do Sul in Brazil, followed by Paraguay, Bolivia, Venezuela, and Argentina. The countries and regions of Brazil do not change, only intensify from year to year, and such fire foci variability may be associated with the El Niño-Southern Oscillation (ENSO) phases. The control scenario identified in east-central Brazil, western Bolivia, Paraguay, and northern Argentina. The optimistic scenario showed an improvement in some countries and a worsening in the territorial distribution in Brazil, Venezuela, and Colombia. The pessimistic scenario identified increased degradation compared to the previous scenarios in almost all SA countries. • Brazil, Bolivia, Argentina, Venezuela, and Paraguay largest records of fire foci in South America. • Projections of future fire risk scenarios indicate central-eastern Brazil, Bolivia, Paraguay, and Argentina. • The integrated analysis identified land use and climatic variability as the main factors. [ABSTRACT FROM AUTHOR]