35 citations as recorded by crossref.

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4. Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review F. Mucomole et al. 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. 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. 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. 10.1016/j.atmosenv.2020.117994
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10. Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. 10.1109/TGRS.2023.3330544
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. 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. 10.1007/s11356-023-28698-0
13. A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. 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. 10.3390/rs15082172
15. Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States H. Zhang et al. 10.5194/amt-6-471-2013
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. 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. 10.3390/rs10071022
20. A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land G. Zhong et al. 10.3390/rs9060524
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22. Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California J. Su et al. 10.1016/j.envint.2024.109100
23. Holistics 3.0 for Health D. Lary et al. 10.3390/ijgi3031023
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. 10.1109/TGRS.2019.2944949
25. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. 10.3390/rs10010137
26. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign M. Choi et al. 10.5194/amt-9-1377-2016
27. Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization C. Jin et al. 10.3390/rs13224689
28. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
29. Utilization of O4 slant column density to derive aerosol layer height from a space-borne UV–visible hyperspectral sensor: sensitivity and case study S. Park et al. 10.5194/acp-16-1987-2016
30. Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land W. Zhang et al. 10.3390/rs11091108
31. Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo X. Ceamanos et al. 10.3390/rs11242958
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. 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. 10.1016/j.ocecoaman.2023.106716
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35. Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment D. Lesser et al. 10.3390/s21010063