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Soil CO2 emission (FCO2) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO2 in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson's correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R2 adj): 0.70 and root mean square error (RMSE): 1.02 µmol m−2 s−1], RP (R2 adj: 0.48 and RMSE: 1.07 µmol m−2 s−1) and GS (R2 adj: 0.70 and RMSE: 1.05 µmol m−2 s−1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing, it is now possible to identify and characterize greenhouse gas emissions under deferment land uses. Given the above, this study aimed to characterize the spatial–temporal variability and the main factors controlling the average atmospheric CO2 column (Xco2) in the macroregion of Ribeirão Preto (MRP), São Paulo, a significant sugarcane producer in Brazil. We obtained remote sensing data from January 2015 to December 2018. The variables used were Xco2 and sun-induced fluorescence of chlorophyll (SIF) by NASA's Orbiting Carbon Observatory-2 satellite (OCO-2), relative humidity (RH), global radiation (Qg), and the average temperature at 2 m (T2m) by the NASA-POWER platform, and leaf area index (LAI) and evapotranspiration by Penman–Monteith (ET) by MODIS sensor. We evaluated the data in trimester's averages, where descriptive statistics, Pearson correlation and linear regression have been applied. The spatial distribution was made by the inverse distance weighted (IDW). The minimum (390.40 ± 0.41 ppm) and maximum (394.75 ± 0.34 ppm) mean of Xco2 was observed in the first quarter of 2015 and third quarter of 2017. The Xco2 obtained negative correlations with the SIF (−0.81), LAI (−0.81), RH (−0.74), ET (−0.84), and Qg (−0.51). Hotspots and coldspots of Xco2 tend to vary over the years. We conclude that the temporal variation of Xco2 above sugarcane areas in southern Brazil is well represented by a periodic function. Our results indicate photosynthesis and soil exposure after harvest are factors that could act as source and sink of CO2. [ABSTRACT FROM AUTHOR]

• For the first time the observations of OCO-2 & 3 was combined to access Xco 2 anomalies. • The Amazon biome has more positive XCO 2 anomalies. • The Brazilian biomes more affected by fire was Amazon, Cerrado and Pantanal. • Xco 2 positive anomalies is correlated with Fire Foci observations. Climate change is a challenge to the global community and one of the causes is the increase of greenhouse gases (GHG) in the atmosphere, especially carbon dioxide (CO 2). The major emission source of this gas into the atmosphere comes from the burning of fossil fuels and biomass burning, on the other hand, the main sink comes from biochemical processes such as photosynthesis. Thus, the observation of CO 2 is a key point to understanding sources and sinks. In this context, The Orbiting Carbon Observatory 2 (OCO-2) and 3 (OCO-3), are a NASA dedicated mission to monitor the column-averaged dry-air mole fraction of carbon dioxide (XCO 2) on a global scale. We combined the OCO-2 and OCO-3 observations to study the spatial distribution of XCO 2 anomalies and how some of these anomalies are related to fire occurrence in the Brazilian Biomes during 2020 and 2021 considering two different seasons, Dry and Wet. The fire occurrence was obtained from Fire Information for Resource Management System (FIRMS) that provides the data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) product of active fires and thermal anomalies at Near-Real Time (MCD14DL, collection 6). The OCO-2/3 observations are affected by cloud formations in wet seasons, we observe that the dry period has more observations. The XCO 2 anomaly values range from ∼ 7.0 ppm to −7.0 ppm and mostly positive anomalies occur in Amazon Biome, and this ecosystem has higher average values for all periods (∼0.9 ppm), compared to the other biomes. The fire occurrence was higher in dry periods, especially in 2020 when unprecedented fire outbreaks were registered in Brazil. The most affected biomes were Pantanal, Cerrado, and Amazon. XCO 2 positive anomalies spatially agree with fire foci over some areas, and the correlation values between them ranged from 0.2 to 0.5 depending on the biome and season, and when considering observations with clouds the correlation is slightly higher. We point out for the first time the possibility of using OCO-2 and 3 combined, also, how positive XCO 2 anomalies are related to fire occurrence in different ecosystems and periods, and the role of cloud detection in this relationship. [ABSTRACT FROM AUTHOR]

Integrated Systems (IS) have been identified as an efficient land-management strategy for restoring degraded areas worldwide, increasing crops and beef yields and providing technical potential for carbon (C) sequestration in soil and trees as an option for offsetting CH 4 and N 2 O emissions from cattle production. The aim of our study is to estimate the greenhouse gas (GHG) balance and the C footprint of beef cattle (fattening cycle) in three contrasting production scenarios on the Brachiaria pasture in Brazil—1) degraded pasture (DP), 2) managed pasture (MP), and 3) the crop-livestock-forest integrated system (CLFIS)—presenting new alternatives of land use as a GHG mitigation strategy. Area-scaled total GHG emissions were highest in MP (84,541 kg CO 2 eq ha −1 ), followed by CLFIS (64,519 kg CO 2 eq ha −1 ) and DP (8004 kg CO 2 eq ha −1 ) over a 10-yr period. Our results note that the highest C footprint of beef cattle was in the DP, 18.5 kg CO 2 eq per kg LW (live weight), followed by 12.6 kg CO 2 eq per kg LW in the CLFIS and 9.4 kg CO 2 eq per kg LW in the MP, without taking into account the technical potential for C sequestration in MP (soil C) and CLFIS (soil and Eucalyptus C). Considering the potential for soil C sequestration in the MP and CLFIS, the C footprint of beef cattle could be reduced to 7.6 and −28.1 kg CO 2 eq per kg LW in the MP and CLFIS, respectively. The conversion of the degraded pasture to a well-managed pasture and the introduction of CLFIS can reduce their associated GHG emissions in terms of kg CO 2 eq emitted per kg of cattle LW produced, increasing the production of meat, grains and timber. This reduction is primarily due to pasture improvement and increases in cattle yields and the provision of technical potential for C sinks in soil and biomass to offset cattle-related emissions. [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]