The recent studies of the variations in the atmospheric column-averaged CO2 concentration ( $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ ) above croplands and forests show a negative correlation between $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 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 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. 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 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ cycle. The daily model of $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ estimated from Qg and RH predicts daily $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). The obtained results imply that a significant part of daily $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 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.

中文翻译：

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用于估计巴西圣保罗州上方每日大气柱平均 CO2 浓度的经验模型

最近对农田和森林上方大气柱平均 CO2 浓度 ( $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ ) 变化的研究表明$${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 与太阳诱导的叶绿素荧光 (SIF) 呈负相关，并证实光合作用是大气 CO2 的陆地吸收。在这种情况下，遥感技术对于观察这种关系非常重要，但是，轨道数据仍然存在时间差距，因为观测不是每天。在这里，我们分析了与植被光合能力相关的几个变量对圣保罗州以上 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 的影响。 2015 年至 2019 年期间，并提出了一个每日模型来估计大气 CO2 的自然变化。从轨道碳观测站 2 (OCO-2)、NASA-POWER 和提取和探索分析就绪样本应用 (AppEEARS) 检索到的数据表明，全球辐射 (Qg)、太阳诱导的叶绿素荧光 (SIF) 和相对湿度(RH) 是预测年度 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 周期的最重要因素。从 Qg 和 RH 估计的 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 的每日模型预测每日 $${\text{X}}_ {{{\text{CO}}_{{2}} }}$$，均方根误差为 0.47 ppm（决定系数等于 0.44，p < 0.01）。