xCO2 temporal variability above Brazilian agroecosystems: A remote sensing approach

Filho, Luiz Fernando Favacho Morais; Meneses, Kamila Cunha de; Santos, Gustavo André de Araújo; Bicalho, Elton da Silva; Rolim, Glauco de Souza; Scala, Newton La

doi:10.1016/j.jenvman.2021.112433

Cited by 12 publications

(11 citation statements)

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“…The natural annual cycle of is affected by factors related to climate and vegetation aspects [ 6 , 36 , 37 ]. Due to the VIF analysis, we were able to summarize three main factors for São Paulo state: Global Radiation (Qg), Relative Humidity (RH) and Sun-Induced chlorophyll Fluorescence at 757 nm (SIF 757), reducing the uncertainties in the model formulation since we removed the overfit caused by multicollinearity [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…1 ), hence, related to the sink of atmospheric CO 2 . The non-significant correlation between and Ws could be related to the detrending of the atmospheric CO 2 concentration (see Methods section), which removes the transport effect and simplify the variability only for the biochemical cycle [ 6 , 7 , 33 ]. In general, the highest concentrations of are observed in the months corresponding to the Brazilian autumn and winter (April to August) and lowest in the summer, from December to February.…”

Section: Discussionmentioning

confidence: 99%

“…Concluding that the dependence of the natural carbon cycle is related to the predominant agriculture crop in the region and how Global Radiation (Qg), relative humidity (RH), and the Sun Induced Chlorophyll Fluorescence (SIF) was related to this behavior. Similarly, Morais Filho et al [ 6 ] conducted a study that analyzed three different crops and the temporal variability of and SIF in these environments, they also found a significant negative correlation between these variables.…”

Section: Introductionmentioning

confidence: 93%

“…In this sense, several studies analyze the average concentration of this greenhouse gas not only on a global scale [ 1 , 2 ] but also to estimate anthropogenic emissions in urban centers [ 3 , 4 ]. Other studies focus on understanding the column-averaged of carbon in the atmosphere ( ) above tropical forests [ 5 ], or above agriculture crops in different seasons of the year [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…One of our assumptions, is given that we detrend the to maintain only the variability related to the natural interactions [ 6 , 33 ], the transport by the wind in the atmosphere is not significant, other studies such Hakkarianen et al [ 34 ], that proposed an anomaly model of , also disregard the atmospheric and wind transport in their study, however, this introduces a limitation of our approach that not account this aspect [ 35 ]. In the same way, the trend and increase due to anthropogenic sources are also simplified by this detrend.…”

Section: Introductionmentioning

confidence: 99%

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An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil

Costa

Santos

Panosso

et al. 2022

Carbon Balance Manage

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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.

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