Air pollution is becoming more serious, especially in developing countries like Vietnam. Air pollution is affecting human health, especially atmospheric particulate matter (PM) with a diameter 2.5 μ" role="presentation">μ
Atmospheric correction, Landsat 8OLI, PM10 concentration
1. Chavez, P. S. (1996) , Image-based atmospheric corrections-revisited and improved, Photogrammetric Engineering and Remote Sensing, 62, no. 9, p. 1025-1035
2. Dinoi, A., M. A. Perrone, P. Burlizzi (2010) , Application of MODIS Products for Air Quality Studies OverSoutheastern Italy, Remote Sensing, 2, p. 1767-1796, https://doi.org/10.3390/rs2071767
3. Forster, B. C. (1984) , Derivation of atmospheric correction procedures for Landsat MSS with particular reference to urban data, International Journal of Remote Sensing, 5, p. 799-817
4. Gao, J., Y. Zha (2010) , Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China, Remote Sensing, 2, p. 2136-2147, https://doi.org/10.3390/rs2092136
5. Hadjimitsis, D. G. (1999) , The application of atmospheric correction algorithms in the satellite remote sensing of reserviors, PhD thesis, Univeristy of Surrey, School of Engineering in the Environment Department of Civil Engineering Guidford, Surrey
6. Hadjimitsis, D. G. (2009) , Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm, Air Quality, Atmosphere & Health, 2, p. 89-97, https://doi.org/10.1007/s11869-009-0036-0
7. Hadjimitsis, D. G., C. R. I. Clayton, V. S. Hope (2004) , An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs, International Journal of Remote Sensing, 25, no. 18, p. 3651-3674, https://doi.org/10.1080/01431160310001647993
8. Hadjimitsis, D. G., C. R. I. Clayton, A. Retalis (2003) , Darkest pixel atmospheric correction algorithm: a revised procedure for environmental applications of satellite remotely sensed imagery, Proceedings of the 10th International Symposium on Remote Sensing, organised by NASA, SPIE Conference, 414, p. 8-12, NASA, Barcelona, Spain
9. Hadjimitsis, D. G., P. T. Kyriacos, P. Giorgos (2008) , The application of atmospheric correction algorithms for monitoring atmospheric pollution using Landsat TM images, Proc. SPIE 7107, Remote Sensing of Clouds and the Atmosphere XIII, 71070H (13 October 2008), SPIE, Cardiff, Wales, United Kingdom, https://doi.org/10.1117/12.800346
10. Hadjimitsis, D. G., A. Retalis, C. R. I. Clayton (2002) , The assessment of atmospheric pollution using satellite remote sensing techniques in large cities in the vicinity of airports, Water, Air and Soil Pollution: Focus. An International Journal of Environmental Pollution, 2, no. 5-6, p. 631-640, https://doi.org/10.1023/A:1021305417004
11. Hadjimitsis, D. G., A. Nisantzi, K. Themistocleous (2010) , Satellite remote sensing, GIS and sun-photometers for monitoring PM10 in Cyprus, Proc. of SPIE, 7826, https://doi.org/10.1117/12.865120
12. Jerome, V., S. Richard, R. Didier (2010) , Atmospheric particulate matter (PM) estimation from SeaWiFS imagery, Remote Sensing of Environment, p. 1-10, https://doi.org/10.1016/j.rse.2007.03.009
13. Kanaroglou, P. S., N. A. Soulakellis, N. I. Sifakis (2002) , Improvement of satellite derived pollution maps with the use of a geostatistical interpolation method, Journal of Geographical Systems, 4, p. 193-208, https://doi.org/10.1007/s101090100080
14. Kaufman, Y. J., R. S. Fraser, R. A. Ferrare (1990) , Satellite remote sensing of large scale air pollution method, Journal of Geophysical Research, 95, p. 9895-9909, https://doi.org/10.1029/JD095iD07p09895
15. Koelemeijer, R. B. A., C. D. Homan, J. Matthijsen (2006) , Comparison of spatial and temporal variations of aerosol opticalthickness and particulate matter over Europe, Atmospheric Environment, 40, p. 5304-5315, https://doi.org/10.1016/j.atmosenv.2006.04.044
16. Kyriacos, T. (2011) , Improving atmospheric correction methods for aerosol optical thickness retrieval supported by in-situ observations and GIS analysis, Doctor of Philosophy Dissertation, Cyprus University of Technology, Limassol
17. Lim, H. S., M. Z. MatJafri, K. Abdulla (2004) , Remote Sensing of PM10 From Landsat TM Imagery, 25th ACRS 2004 Chiang Mai, p. 739-744, Chiang Mai, Thailand
18. Lim, H. S., Z. M. J. Mohd (2007) , Mapping atmospheric pollution using remote sensing, SPIE Newsroom, https://doi.org/10.1117/2.1200710.0853
19. Moran, M. S., D. J. Ray, N. S. Philip, et at. (1992) , Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output, Remote Sensing of Environment, 41, no. 2-3, p. 169-184, https://doi.org/10.1016/0034-4257(92)90076-V
20. Nadzri, O., Z. M. J. Mohd, H. S. Lim (2010) , Estimating Particulate MatterConcentration over Arid Region Using Satellite Remote Sensing: A CaseStudy in Makkah, Saudi Arabia, Modern Applied Science, 4, p. 131-142, https://doi.org/10.5539/mas.v4n11p131
21. Nordio, F., I. Kloog, B. Coull, et at. (2013) , Estimating spatio-temporal resolved PM10 aerosol massconcentrations using MODIS satellite data and land use regression over Lombardy, Italy, Atmospheric Environment, 74, p. 227-236, https://doi.org/10.1016/j.atmosenv.2013.03.043
22. Roy, A., A. Jivani, B. Parekh (2017) , Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data, International Journal of Advanced Remote Sensing and GIS, 6, no. 1, p. 2246-2252, https://doi.org/10.23953/cloud.ijarsg.284
23. Sifakis, N., P. Y. Deschamps (1992) , Mapping of Air Pollution Using SPOT Satellite, Photogrammetric Engineering & Remote Sensing, 5, p. 4
24. Sobrino, J. A., C. J-M. Juan, L. Paolini (2004) , Land surface temperature retrieval from Landsat TM 5, Remote Sensing of Environment, 90, p. 434-440, https://doi.org/10.1016/j.rse.2004.02.003
25. Zhang, L., et al. (2015) , Retrieval of aerosol optical depth over Beijing using Landsat8/OLI data, Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII, p. 961,011, SPIE Optical Engineering + Applications, San Diego, California, https://doi.org/10.1117/12.2186761