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Cross-Validation of Collocated ICESat-2 and CALIPSO Cloud-Aerosol Discrimination
Journal article   Open access   Peer reviewed

Cross-Validation of Collocated ICESat-2 and CALIPSO Cloud-Aerosol Discrimination

Chase A. Fuller, Shi Kuang, Patrick A. Selmer, Joseph Gomes and Matthew J. McGill
Remote sensing (Basel, Switzerland), Vol.18(18), 3167
09/15/2026
DOI: 10.3390/rs18183167
url
https://doi.org/10.3390/rs18183167View
Published (Version of record) Open Access

Abstract

This study presents a feature-scale cross-validation of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) cloud–aerosol discrimination (CAD) products using 9289 globally collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations acquired between October 2018 and June 2023. The operational ATL09 and a U-Net convolutional neural network (CNN) product are evaluated against CALIPSO on a pixel-by-pixel basis using class-specific metrics. Agreement improves with decreasing along-track comparison-window width, which limits spatial divergence from the ground-track crossing, whereas agreement varies weakly and non-monotonically across the actual 0–10 min inter-satellite time separation. Agreement also depends strongly on the CALIPSO integration size used for layer detection, which affects both feature detectability and effective spatial resolution. Restricting the analysis to ±60° latitude markedly increases aerosol agreement, consistent with reduced influence from high-latitude ambiguities associated with blowing snow, diamond dust, and larger crossing angles, while cloud metrics change more modestly. At a 1-s comparison width and 5-km maximum CALIPSO integration scale, CNN–CALIPSO F1 exceeds ATL09–CALIPSO F1 by 0.027–0.045 globally, with all paired 95% confidence intervals above zero across cloud and aerosol classes under daytime and nighttime conditions; however, some differences within ±60° are not distinguishable from zero. These results show that agreement is governed not only by retrieval algorithms but also by differences in instrument characteristics, effective spatial resolution, and sampling strategy. The proposed collocation framework provides a basis for evaluating current and future machine-learning approaches for spaceborne lidar CAD.
 aerosol atmospheric classification CALIPSO cloud-aerosol discrimination convolutional neural network deep learning ICESat-2 lidar machine learning

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