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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用于估算巴西圣保罗州上空每日大气柱平均二氧化碳浓度的经验模型


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$${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 与太阳诱导叶绿素荧光 (SIF) 之间呈负相关，并证实光合作用是陆地对大气二氧化碳的吸收。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间隙，因为观测不是每天进行的。在此，我们分析了圣保罗州上空 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 期间与植被光合能力相关的几个变量的影响2015年至2019年期间，并提出了一个每日模型来估计大气二氧化碳的自然变化。从轨道碳观测站 2 (OCO-2)、NASA-POWER 和提取和探索分析就绪样品应用程序 (AppEEARS) 检索的数据显示，全球辐射 (Qg)、太阳诱导叶绿素荧光 (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。根据 Qg 和 RH 估计的 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 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）。所获得的结果意味着每日 $${\text{X}}_{{{\text{CO}}_{{2}} }}$$ 变化的很大一部分可以通过气象因素来解释，并且进一步研究应量化大气传输和人为排放的影响。