12 citations as recorded by crossref.

1. Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models Z. Zang et al. 10.3390/rs13142779
2. Air Quality over China G. de Leeuw et al. 10.3390/rs13173542
3. Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
4. Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano A. Bedoya-Velásquez et al. 10.3390/rs14225680
5. Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
6. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. 10.1016/j.isprsjprs.2022.09.001
7. Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. 10.1016/j.envpol.2024.123838
8. Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. 10.1016/j.atmosenv.2022.118970
9. A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
10. Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data Z. Shi et al. 10.1109/TGRS.2022.3192908
11. A MISR-Based Method for the Estimation of Particle Size Distribution: Comparison with AERONET over China Y. Shao et al. 10.34133/remotesensing.0032
12. A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. 10.5194/essd-14-1193-2022