Global Aerosol Climatology from ICESat-2 Lidar Observations

Global Aerosol Climatology from ICESat-2 Lidar Observations

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite I...

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Bibliographic Details
Main Authors: Shi Kuang, Matthew McGill, Joseph Gomes, Patrick Selmer, Grant Finneman, Jackson Begolka
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Remote Sensing
Subjects:
aerosol
cloud–aerosol discrimination
deep learning
ICESat-2
lidar
machine learning
Online Access:https://www.mdpi.com/2072-4292/17/13/2240
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