da Costa Luis Miguel, de Araújo Santos Gustavo André, Panosso Alan Rodrigo, de Souza Rolim Glauco, La Scala Newton
Departament of Engineering and Exact Sciences, São Paulo State University, Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, São Paulo, 14884-900, Brazil.
Campus Avançado Porto Franco, Instituto Federal de Educação, Ciência e Tecnologia do Maranhão - IFMA, Rua Custódio Barbosa, no 09, Centro, Porto Franco, Maranhão, 65970-000, Brazil.
Carbon Balance Manag. 2022 Jun 11;17(1):9. doi: 10.1186/s13021-022-00209-7.
The recent studies of the variations in the atmospheric column-averaged CO concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO. 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 [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO.
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 [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] 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 [Formula: see text] 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.
近期对农田和森林上空大气柱平均一氧化碳浓度([公式:见正文])变化的研究表明,[公式:见正文]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吸收大气中一氧化碳的主要调节因素。在此背景下,遥感技术对于观测这种关系非常重要,然而,由于观测并非每日进行,轨道数据仍存在时间间隔。在此,我们分析了2015年至2019年期间与植被光合能力相关的几个变量对圣保罗州上空[公式:见正文]的影响,并提出了一个每日模型来估算大气中一氧化碳的自然变化。
从轨道碳观测站 - 2(OCO - 2)、美国国家航空航天局功率(NASA - POWER)以及提取和探索分析就绪样本应用程序(AppEEARS)获取的数据表明,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[公式:见正文]周期的最重要因素。根据Qg和RH估算的[公式:见正文]每日模型预测每日[公式:见正文]的均方根误差为0.47 ppm(决定系数等于0.44,p < 0.01)。
所得结果表明,每日[公式:见正文]变化的很大一部分可以由气象因素解释,并且应该进一步开展研究以量化大气传输和人为排放的影响。