Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm

Aerosol size information is important to the understanding of aerosol dynamics, which change rapidly during wildfire, dust transport, and volcanic eruption events over Asia. In this study, a deep neural network (DNN) model was trained using Advanced Meteorological Imager (AMI) Level 1B observations,...

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Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 12
Main Authors: Kim, Minseok, Kim, Jhoon, Lee, Seoyoung, Lim, Hyunkwang, Cho, Yeseul, Chul Lee, Hyun, Keun Kuk, Su
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerosol
Aerosol Robotic Network
Aerosols
Algorithms
Artificial neural networks
Asia
Atmospheric particulates
Deconvolution
deep neural network (DNN)
Dust
Earth
Earth orbit
Earth orbits
fine-mode fraction (FMF)
geostationary satellite
MODIS
Neural networks
Optical analysis
Optical imaging
Optical reflection
Optical sensors
Optical thickness
Reflectivity
Retrieval
Root-mean-square errors
Satellite observation
Satellites
spectral deconvolution algorithm (SDA)
Training
Volcanic eruptions
Wildfires
Asia
ISSN:0196-2892, 1558-0644
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Summary:Aerosol size information is important to the understanding of aerosol dynamics, which change rapidly during wildfire, dust transport, and volcanic eruption events over Asia. In this study, a deep neural network (DNN) model was trained using Advanced Meteorological Imager (AMI) Level 1B observations, AMI Yonsei aerosol retrieval (YAER) aerosol products, and observation geometries to retrieve the aerosol optical depth (AOD),Ångström exponent (AE), and spectral derivatives of AE (AE<inline-formula> <tex-math notation="LaTeX">{}^{\prime } </tex-math></inline-formula>). The fine-mode fraction (FMF) was calculated with a spectral deconvolution algorithm (SDA) using retrieved AE and AE<inline-formula> <tex-math notation="LaTeX">{}^{\prime } </tex-math></inline-formula> when AOD >0.2. The retrieved aerosol products were validated using Aerosol RObotic NETwork (AERONET) (AOD at 550 nm: <inline-formula> <tex-math notation="LaTeX">R =0.837 </tex-math></inline-formula>, root-mean-square error (RMSE) =0.219, and mean bias error (MBE) <inline-formula> <tex-math notation="LaTeX">= -0.066 </tex-math></inline-formula>; AE: <inline-formula> <tex-math notation="LaTeX">R =0.726 </tex-math></inline-formula>; RMSE =0.231; MBE <inline-formula> <tex-math notation="LaTeX">= -0.007 </tex-math></inline-formula>; FMF: R =0.875; RMSE =0.072; and MBE =0.007). Case studies of dust transport, wildfire, and haze events in Asia revealed that the retrieved aerosol size products may be used for analysis of sudden pollution events. Results of this study indicate the potential for a comprehensive analysis of aerosol properties in Asia using continuous aerosol size data from geostationary Earth orbit (GEO) satellite observations.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3591177