Cross-Validation of Collocated ICESat-2 and CALIPSO Cloud-Aerosol Discrimination.
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| Title: | Cross-Validation of Collocated ICESat-2 and CALIPSO Cloud-Aerosol Discrimination. |
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| Authors: | Fuller, Chase A.1 (AUTHOR), Kuang, Shi1,2 (AUTHOR) shi-kuang@uiowa.edu, Selmer, Patrick A.2,3 (AUTHOR), Gomes, Joseph1,3 (AUTHOR), McGill, Matthew J.1,2,3 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3167. 25p. |
| Subjects: | Spatial resolution, Convolutional neural networks, Machine learning, LIDAR, Model validation |
| Abstract: | Highlights: What are the main findings? Pixel-by-pixel agreement between spaceborne lidar products depends strongly on their effective spatial resolutions. The U-Net CNN shows higher cloud–aerosol classification agreement with CALIPSO than the operational ATL09 product. What are the implications of the main findings? Spaceborne lidar validation should account for spatial resolution, SNR, and sampling strategy in interpreting classification metrics. The feature-scale collocation framework provides a basis for evaluating machine-learning methods and future spaceborne lidar products. 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) products 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Pixel-by-pixel agreement between spaceborne lidar products depends strongly on their effective spatial resolutions. The U-Net CNN shows higher cloud–aerosol classification agreement with CALIPSO than the operational ATL09 product. What are the implications of the main findings? Spaceborne lidar validation should account for spatial resolution, SNR, and sampling strategy in interpreting classification metrics. The feature-scale collocation framework provides a basis for evaluating machine-learning methods and future spaceborne lidar products. 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) products 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18183167 |
