12 citations as recorded by crossref.

1. Aerosol Absorption Over Land Derived From the Ultra-Violet Aerosol Index by Deep Learning J. Sun et al. https://doi.org/10.1109/JSTARS.2021.3108669
2. Intelligent Atmospheric Attenuation Mitigation in Terahertz Satellite Communication Using Adaptive Image Processing of Hyperspectral Data X. Cui & X. Zhang https://doi.org/10.1007/s10762-025-01102-3
3. Application of airborne and spaceborne hyperspectral imaging techniques for atmospheric research: past, present, and future M. Calin et al. https://doi.org/10.1080/05704928.2020.1774381
4. Retrieving vertical profiles of cloud droplet effective radius using multispectral measurements from MODIS: examples and limitations A. Buggee & P. Pilewskie https://doi.org/10.5194/amt-18-5299-2025
5. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu https://doi.org/10.5194/acp-20-12853-2020
6. The Spectral Nature of Earth’s Reflected Radiation: Measurement and Science Applications G. Stephens et al. https://doi.org/10.3389/frsen.2021.664291
7. Hyperspectral imaging D. Hong et al. https://doi.org/10.1038/s43586-026-00470-x
8. MODIS aerosol optical depth retrieval based on random forest approach T. Liang et al. https://doi.org/10.1080/2150704X.2020.1842540
9. Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models F. Yu et al. https://doi.org/10.1029/2022GL098551
10. Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning H. Choi et al. https://doi.org/10.1016/j.scitotenv.2025.180535
11. Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning T. Liang et al. https://doi.org/10.3390/rs14051053
12. A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119098