Dynamics of atmospheric carbon dioxide over different land cover types in India

Chhabra, Abha; Gohel, Ankit

doi:10.1007/s10661-019-7681-z

Cited by 15 publications

(1 citation statement)

References 38 publications

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“…Several studies con rm that photosynthesis is the main regulator of atmospheric carbon sinks [10,24,25,26]. However, photosynthesis is a process sensitive to climatic variations such as relative humidity [27], precipitation [28], evapotranspiration [29], and incident solar irradiance [30].…”

Section: Introductionmentioning

confidence: 99%

Daily Model of Xco2 Based on Orbital Observations for the State of São Paulo – Brazil

Costa

Santos

Panosso

et al. 2022

Preprint

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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.

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