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A Remote Sensing-Based Method for Generating a Global Continuous Carbon Dioxide Concentration Dataset
  • Huilin Sun,
  • Xuecao Li,
  • Kevin R Gurney
Huilin Sun
School of Informatics, Computing & Cyber Systems, Northern Arizona University

Corresponding Author:[email protected]

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Xuecao Li
College of Land Science and Technology, China Agricultural University
Kevin R Gurney
School of Informatics, Computing & Cyber Systems, Northern Arizona University


The global carbon dioxide (CO2) concentration has shown a consistent and substantial increase over the years, representing the dominant component of greenhouse gases (GHGs). Hence, there is an urgent demand to accurately quantify a broad spectrum of CO2 concentration at a fine-scale level to aid policymakers in making informed decisions. Consequently, we present a novel method aimed at addressing the scarcity of ground-based data, enabling the generation of a globally large-scale, continuous CO2 concentration data product.
In consideration of the requirements for temporal and spatial coverage of remote sensing imagery, we opt for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, which provides daily surface reflectance of MODIS bands 1 to 7 at resolutions of 500m and 1km.
Carbon satellites have developed rapidly and performed well in retrieving the vertically integrated atmospheric column CO2(XCO2) concentrations, which can provide independent top-down CO2 concentration evaluations. Here, the new generated Orbiting Carbon Observatory 3 (OCO-3) with 1.6 km×2.2 km (across × along track) resolution is added three Near Infrared (NIR) wavelength bands, which guarantees a higher accuracy of XCO2 than OCO-2.
In this study, we propose a regression model-based method that leverages MODIS data and OCO-3 XCO2 data for training regression models and predicting CO2 concentrations. The proposed method enables rapid establishment of the relationship between MODIS surface reflectivity and CO2 concentration, facilitating the generation of continuous CO2 concentration maps over a large geographical area. Moreover, it offers reliable information for regions lacking ground-based CO2 measurements, such as suburban areas.
Additionally, to validate the accuracy of the generated XCO2 data product, we utilize the Total Carbon Column Observing Network (TCCON) as an essential validation source. Upon evaluation, it was observed that the relative errors for each month of the year 2020 at the respective TCCON  sites consistently remained below 2%.  This finding suggests that the proposed method possesses the potential for expansion to additional geographical regions and temporal spans, whilst sustaining a high level of precision.
04 Jan 2024Submitted to ESS Open Archive
13 Jan 2024Published in ESS Open Archive