AI-enhanced satellite carbon monoxide fast retrieval

    KNOXVILLE, TN, December 21, 2024 /24-7PressRelease/ -- A recent study presents a radiative transfer model-driven machine learning technique for retrieving carbon monoxide from the world's first hyperspectral Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B (FY-4B) satellite, providing complementary insights into air quality and pollutant transport over East Asia.

FY-4B/GIIRS scans East Asia every two hours during both daytime and nighttime. Its measured radiation contains information on atmospheric temperature, humidity, and a variety of reactive trace gases. The massive data from the broad detection region and time-intensive scanning challenge the real-time retrieval of these atmospheric parameters. The study published (DOI: 10.34133/remotesensing.0289) on November 1, 2024, in the Journal of Remote Sensing, takes carbon monoxide, a strong absorbing reactive trace gas, as an example to explore the reliability of retrieval using an efficient machine learning method compared to traditional physical method.

The core idea of this machine learning approach is to rapidly convert the CO spectral features extracted from GIIRS measurements into columns through a trained model and simultaneously estimate the uncertainty based on the error propagation theory. The model is trained in spatially and temporally representative radiative transfer simulations. Comparisons with the retrieval results of traditional physical methods and ground-based observations reveal consistent spatial distribution and temporal variation across these different datasets.

Dr. Dasa Gu, a leading researcher on the project, stated that "our results confirm that machine learning methods have the potential to provide reliable CO products without the computationally intensive iterative process required by traditional retrieval methods. However, characterizing the instrument sensitivity of machine learning retrieval results is one issue that needs to be addressed before operational retrieval."

References

DOI
10.34133/remotesensing.0289

Original Source URL
https://doi.org/10.34133/remotesensing.0289

Funding information
The work is supported by the Hong Kong Research Grants Council (26304921, 16306224) and Hong Kong Environment and Conservation Fund (78-2019, 13-2022). C.Z. and M.Z. are supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDB41000000) and the USTC Research Funds of the Double First-Class Initiative.

About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.

Contact
Duoduo Li, Journal of Remote Sensing, [email protected]

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