Background: The recent studies of the variations in the atmospheric column-averaged CO₂ concentration ( $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 ) above croplands and forests show a negative correlation between $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO₂. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO₂. Results: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 cycle. The daily model of $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 estimated from Qg and RH predicts daily $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). Conclusion: The obtained results imply that a significant part of daily $${\text{X}}_{{{\text{CO}}_{{2}} }}$$X CO 2 variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Background: Understanding the behavior of carbon dioxide (CO₂) it is crucial to create strategies to deal with climate change. Several space missions were designed to monitor this and other greenhouse gases and studies at global and large regional scales were developed using these remote sensing tools. However, there is still a time gap in orbital data, since the revisit time of a satellite at the same location for this type of observation is not daily. Daily measurements of CO₂ can be made using Eddy Covariance technique, although this type of study is at a very local scale. In this study, we aimed to build a daily model to estimate the natural CO₂ in the atmosphere.Results: The data was retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and, Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) and consist in a time-series from 2015 to 2019. To summarize the most relevant factor we used the Variance Inflation Factor (VIF) after we performed the Pearson’s Correlation and apply descriptive statistics in the significant variables (p < 0.05). The model was construed using the stepwise regression method and the selected model was defined by the lowest RMSE in training (~0.6 ppm). The most related variables with Xco₂ were Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH), all these factors were negatively correlated with the CO₂ concentration. The model has the best perform with the Qg and RH (RMSE = 0.47 ppm, R² = 0.44, p < 0.01).Conclusion: In summary, the cycle of atmospheric CO₂ in the state of São Paulo has higher average values during April to October, and the lowest averages of Xco₂ were usually observed between December to March and the inverse behavior was observed for SIF 757, Global Radiation (Qg) and Relative Humidity (RH). Concerning the daily model, despite the differences between the spatial observations, the model derived here was capable to estimate the cycle of atmospheric CO₂ using only meteorological data.