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The emission of soil carbon dioxide (CO 2 ) 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 soil CO 2 emissions and its relationship with related variables, such as the CO 2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO 2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO 2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO 2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO 2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO 2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO 2 emissions and soil temperature (-0.240) and CO 2 flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO 2 flux model variable. Soil CO 2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m -2 s -1 ) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m -2 s -1 ) and higher for native forests (from 2.279 to 8.131 μmol m -2 s -1 ), whereas the opposite was true for the CO 2 flux model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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% (R 2 adj = 0.58; p < 0.001) and precipitation in 27% (R 2 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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (Copyright © 2022 Elsevier Inc. All rights reserved.)

The objectives of this study are: (i) to evaluate the space-temporal variability of fire foci by environmental satellites, CHIRPS and remote sensing products based on applied statistics, and (ii) to identify the relational pattern between the distribution of fire foci and the environmental, meteorological, and socioeconomic variables in the mesoregions of Minas Gerais (MG) - Brazil. This study used a time series of fire foci from 1998 to 2015 via BDQueimadas. The temporal record of fire foci was evaluated by Mann-Kendall (MK), Pettitt (P), Shapiro-Wilk (SW), and Bartlett (B) tests. The spatial distribution by burned area (MCD64A1-MODIS) and the Kernel density - (radius 20 km) were estimated. The environmental variables analyzed were: rainfall (mm) and maximum temperature (°C), besides proxies to vegetation canopy: NDVI, SAVI, and EVI. PCA was applied to explain the interaction between fire foci and demographic, environmental, and geographical variables for MG. The MK test indicated a significant increasing trend in fire foci in MG. The SW and B tests were significant for non-normality and homogeneity of data. The P test pointed to abrupt changes in the 2001 and 2002 cycles (El Niño and La Niña moderated), which contributes to the annual increase and in winter and spring, which is identified by the Kernel density maps. Burned areas highlighted the northern and northwestern regions of MG, Triângulo Mineiro, Jequitinhonha, and South/Southwest MG, in the 3rd quarter (increased 17%) and the 4th quarter (increased 88%). The PCA resulted in three PCs that explained 71.49% of the total variation. The SAVI was the variable that stood out, with 11.12% of the total variation, followed by Belo Horizonte, the most representative in MG. We emphasize that the applied conceptual theoretical model defined here can act in the environmental management of fire risk. However, public policies should follow the technical-scientific guidelines in the mitigation of the resulting socioeconomic - environmental damages., (Copyright © 2020 Elsevier Ltd. All rights reserved.)