34 citations as recorded by crossref.

1. An Improved Aerosol Retrieval Algorithm for FY-4A/AGRI Data Based on the GRASP Framework H. Wang et al. https://doi.org/10.1109/TGRS.2025.3546614
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4. Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. https://doi.org/10.3390/en18061460
5. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. https://doi.org/10.5194/amt-13-3873-2020
6. Spectral and diurnal temporal suitability of GOES Advanced Baseline Imager (ABI) reflectance for burned area mapping D. Roy et al. https://doi.org/10.1016/j.jag.2020.102271
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8. Assessment of Aerosol optical depth under background and polluted conditions using AERONET and VIIRS datasets M. Kim et al. https://doi.org/10.1016/j.atmosenv.2020.117994
9. Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data L. She et al. https://doi.org/10.1109/TGRS.2018.2867000
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11. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. https://doi.org/10.1109/TGRS.2021.3107542
12. Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand P. Wongnakae et al. https://doi.org/10.1007/s11356-023-28698-0
13. A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2018.11.023
14. Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region J. Zhou et al. https://doi.org/10.3390/rs15082172
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16. Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons J. Yao et al. https://doi.org/10.1021/acs.est.8b01921
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19. Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network W. Qin et al. https://doi.org/10.3390/rs10071022
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21. Global observations of aerosol-cloud-precipitation-climate interactions D. Rosenfeld et al. https://doi.org/10.1002/2013RG000441
22. Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California J. Su et al. https://doi.org/10.1016/j.envint.2024.109100
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24. Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
25. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. https://doi.org/10.3390/rs10010137
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27. Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization C. Jin et al. https://doi.org/10.3390/rs13224689
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32. Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction M. Kim et al. https://doi.org/10.1016/j.rse.2013.12.003
33. Assessment of long-term trends in chlorophyll-a and sea surface temperature in the Arabian Sea and their association with aerosols using remote sensing Y. Sun et al. https://doi.org/10.1016/j.ocecoaman.2023.106716
34. Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application Y. Fan et al. https://doi.org/10.1080/01431161.2024.2398821