Retrieval of Global Carbon Dioxide From TanSat Satellite and Comprehensive Validation With TCCON Measurements and Satellite Observations

Abstract

To cope with global climate change and monitor global CO 2 concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO 2 retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum A Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO 2 spectral retrieval. The global terrestrial CO 2 total vertical column density (VCD) and column-averaged dry-air mole fractions of CO 2 (X CO2 ) were simultaneously retrieved Manuscript

Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

“…Satellites provide the most effective way of monitoring atmospheric CO 2 with great spatiotemporal resolution. Several satellites such as the Greenhouse Gases Observing Satellite (GOSAT), GOSAT-2, the Orbiting Carbon Observatory-2 (OCO-2), and TanSAT are orbiting the Earth and are dedicated to monitoring atmospheric CO 2 (Crisp, 2015;Liu et al, 2018;Matsunaga et al, 2019;Taylor et al, 2020;Bao et al, 2020;Hong et al, 2021;Yang et al, 2018). These satellites calculate the average atmospheric CO 2 concentration in the path of sunlight reflected by the surface using spectrometers carried onboard.…”

“…This increment in the CO 2 levels is mainly due to the rapid economic growth and anthropogenic activities (Shan et al, 2018). China has pledged to make aggressive efforts to reduce the CO 2 emissions per unit gross domestic product (GDP) by 60 %-65 % relative to 2005 levels, and peak carbon emissions overall, by 2030 (Horowitz, 2016). West Asia is also a region with higher rates of anthropogenic CO 2 emissions (Mustafa et al, 2020), and some of its countries, such as Iran, Saudi Arabia, and Turkey, are listed among the 10 largest CO 2 emitting nations in the world.…”

A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China

“…With the development of remote sensing technology, carbon monitoring satellites have realized the dynamic monitoring of large-scale greenhouse gas emissions (e.g., greenhouse gas observation satellites, GOSAT). GOSAT's sub-satellite spatial resolution is approximately 10.5 km, and it is suitable for monitoring carbon dioxide concentrations on region and global 24 . So, GOSAT images are not suitable for studying urban-scale CEs.…”

On-Orbit Characterization of TanSat Instrument Line Shape Using Observed Solar Spectra

“…TanSat is dedicated to retrieving the XCO2 with a precision of 1-4 ppm [3]. Retrieval algorithms based on optimal estimation [10,11] and the IMAP-DOAS algorithm [12] have been developed. These algorithms perform additional spectral and radiometric calibration prior to the spectral fitting.…”

“…Validation against ground-based FTIR measurements from TCCON showed good agreements between TanSat and TCCON, with standard deviations ranging from 1.47 to 2.45 ppm for different algorithms. These algorithms use the preflight tabulated ILS [10] or a super-Gaussian function derived through a cross-correlation of the measured solar spectrum to a high-resolution solar reference spectrum [12]. However, the precalibrated ILS could be changed due to the vibration during launch, orbital movement, thermal variation and instrument degradation; fitting a different ILS function form may induce artificial error due to the undersampling effect [13].…”