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December 2, 2024 This article has been reviewed according to Science X's editorial process and policies . Editors have highlightedthe following attributes while ensuring the content's credibility: fact-checked proofread by Journal of Remote Sensing 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 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 methods.
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, "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.
" More information: Zhenxing Liang et al, Diurnal Carbon Monoxide Retrieval from FY-4B/GIIRS Using a Novel Machine Learning Method, Journal of Remote Sensing (2024). DOI: 10.34133/remotesensing.
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