Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula

Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps tha...

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Bibliographic Details
Main Authors: Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
aerosol optical depth (AOD)
Himawari-8
gap-filling
machine learning
Online Access:https://www.mdpi.com/2072-4292/16/23/4400
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