A dataset of global CO2 concentration in global middle and low latitudes based on GOSAT satellite data during 2009–2020

Abstract

In response to the increasingly intensifying greenhouse effect, Countries around the world jointly signed the Paris Agreement, and China also made plans and policies of peak carbon dioxide emissions, carbon neutral plans. Carbon dioxide is the focus of international concern as the most important greenhouse gas. So, it’s crucial to know how to obtain carbon dioxide concentration of high precision, high resolution temporal and spatial distribution for advancing the top-down assessment of carbon source, carbon sink and carbon neutral research. This paper creatively proposes a method to obtain high-precision and high-resolution temporal and spatial distribution map of global carbon dioxide concentration by using satellite data. We constructed a new prior time curve parameter library for fitting time domain information. In this paper, we used the transfer learning theory to integrate the time information as a prior profile into the spatial information based on the global data of GOSAT satellite. The spatial prediction information was adjusted to obtain more accurate spatio-temporal prediction of carbon dioxide concentration. The spatio-temporal resolution of the product database is 0.25°. Finally, the database has been compared with TCCON data in middle and low latitudes, which shows the correlation coefficient R and RMSE is 0.98 and 1.38 ppm of the monthly average carbon dioxide concentration respectively. The recommended database can be applied to the calculation of carbon sources and carbon sinks on a large scale.