Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method

Lina Xu; Siyu Liu; Minghui Tao; Jun Wang; Xincai Chang; Wenjing Man; Jie Ma; Huang Zhang; Jhoon Kim

doi:10.1016/j.jag.2026.105530

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Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method

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Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method

Lina Xu, Siyu Liu, Minghui Tao, Jun Wang, Xincai Chang, Wenjing Man, Jie Ma, Huang Zhang and Jhoon Kim

International journal of applied earth observation and geoinformation, Vol.153, 105530

09/2026

DOI: 10.1016/j.jag.2026.105530

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Abstract

•DBN retrieves multiple aerosol optical and microphysical parameters from H8/AHI.•DBN improves AOD and FAOD retrieval accuracy over the operational JAXA product.•Spatiotemporal validation demonstrates the model’s robust generalization.•Retrievals characterize major aerosol properties and diurnal variations over East Asia. The high temporal resolution of Himawari-8 (H8) geostationary satellite has distinctive advantages in capturing diurnal variation of aerosol properties. However, the operational H8 aerosol products including Aerosol Optical Depth (AOD) are subject to considerable uncertainties due to assumptions and simplified surface reflectance. To fully exploit the H8 multi-spectral measurements, a data-driven retrieval framework based on a Deep Belief Network (DBN) was developed to simultaneously retrieve aerosol optical and microphysical parameters. By directly modeling H8 spectral reflectance at the top of the atmosphere (TOA) with matched AERONET products in 82 sites across diverse surface types and emission sources, four aerosol parameters including AOD, fine and coarse AOD (FAOD and CAOD), and single scattering albedo (SSA) are retrieved. Ground-based validation shows that the H8 DBN retrievals achieve high accuracies for AOD and FAOD, with correlation coefficients of 0.935 and 0.930, respectively, substantially outperforming the operational JAXA product (R = 0.703 for AOD). The retrieved CAOD and SSA also show good agreement with AERONET observations. Comparisons with MODIS and JAXA H8 aerosol products show very high consistency in both spatial and temporal variations. H8 DBN retrievals can clearly distinguish biomass burning smoke and dust plumes by their particle size and absorption. Additionally, hourly H8 DBN retrievals accurately reflect diurnal variations of aerosol properties. Consequently, the developed DBN-based approach provides a flexible and robust retrieval method for dynamic aerosol properties over East Asia.

Aerosols

Deep Belief Network (DBN)

Himawari-8

Retrieval algorithm

Details

Title: Subtitle: Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method
Creators: Lina Xu - China University of Geosciences
Siyu Liu - China University of Geosciences
Minghui Tao - China University of Geosciences
Jun Wang - University of Iowa
Xincai Chang - China University of Geosciences
Wenjing Man - Hubei Normal University
Jie Ma - China University of Geosciences
Huang Zhang - China University of Geosciences
Jhoon Kim - Yonsei University
Resource Type: Journal article
Publication Details: International journal of applied earth observation and geoinformation, Vol.153, 105530
DOI: 10.1016/j.jag.2026.105530
ISSN: 1569-8432
eISSN: 1872-826X
Publisher: Elsevier B.V
Language: English
Date published: 09/2026
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9985222681402771

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