Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Spectral Response of Multispectral Sensors to Remote Sensing Based PM10 Retrieval

Author : Ajay N Roy 1 Anjali G. Jivani 2 Bhuvan S. Parekh 3

PDF

Date of Publication :13th September 2017

Abstract: The spectral characteristics are a key to remote sensing applications. The extraction of meaningful information from the imagery necessitates good knowledge and understanding of the spectral characteristics of the satellite sensors. Multispectral satellite sensors record information in wide of spectral channels. The spectral response in specific wavelength plays a major role in remote sensing applications. PM10 estimation using remote sensing is now becoming popular approach for pollution monitoring. Several approaches have been used for estimation of PM10 using various satellite sensors. This paper presents the sensitivity of satellite sensors at different wavelengths and its application to estimate PM10. The atmospheric absorption, reflectance and transmission behavior for Visible and Infrared ranges is a key to particulate matter monitoring. The sensitivity of different channels of MODIS, MERIS, SPOT5, Landsat TM/ETM+ and Landsat 8 have been analyzed. The sensor characteristics have also been presented to find the suitability of these sensors for PM10 distribution.

Reference :

    1. B. Husar, M. Holloway, and E. Patterson, “Spatial and temporal pattern of eastern U.S. haziness: A summary,” Atmospheric Environment, vol. 15, pp. 1919- 1928, Nov 1981.
    2. S. Ayub, and S.K. Sharma, “Particulate matter emission from thermal power plant: parent material, formation mechanism, health concerns, control devices – A Review,” Global Journal Engineering and Applied Sciences, vol. 1, pp. 2249-2623, Jan 2000.
    3. Zheng J., Che W., Zheng Z., Chen L., Zhong L., “Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China” Aerosol and Air Quality Research, vol. 13, pp. 862–876, Jan 2013.
    4. M. Z. He, Z. Xiange, Z, Zhang and P. L. Kinney, “Fine particulate matter concentrations in urban Chinese cities, 2005-2016: A systematic review,” International Journal of environment research and public health, vol. 14, pp. 191, Feb 2017.
    5. S. I. Hey, “An overview of remote sensing and geodesy for epidemiology and public health application,” Advances in Parasitology, Vol. 47, pp. 1–35, 2000.
    6. J. L. Awange, and J. B. K. Kiema, „Fundamentals of Remote Sensing,” Environmental Geoinformatics, pp. vol. 125, 111-118, June 2013.
    7. W.H. Bakker, L.L.F. Janssen, and C.V. Reeves, “Principles of Remote Sensing”, The International Institute of Geo-Information Science and Earth Observation (ITC),Vol. 2, pp. 66-80, 2001.
    8. S. Aggarwal, “Principles of remote sensing, Satellite Remote Sensing and GIS applications in agricultural meteorology Dehra Dun, India,” World Meteorological Organisation, Switzerland, pp. 23–38, July 2004.
    9. J. R. Jenson, “Remote Sensing of the Environment: An Earth Resource Perspective,” Person Prentice Hall. May 2007.
    10. J.B. Campbell, and R. H. Wynne, “Introduction to Remote Sensing,” The Guilford Press, New York. Jun 2011.
    11. T.M. Lillesand, R.W. Kiefer, and J. Chipman, “Remote Sensing and Image Interpretation,” Wiley publication, New York. Jan 2015.
    12. R.M. Hoff, and S.A. Christopher, “Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?,” Journal of the Air & Waste Management Association, vol. 59, pp. 645-675, Oct 2009.
    13. D. G. Streets, T. Canty, G. R. Carmichael, B. Foy, R. R. Dickerson, B. N. Duncun et al., “Emissions estimation from satellite retrievals: A review of current capability,” Atmospheric Environment, vol. 77, pp. 1011- 1042, May 2013.
    14. K. A. Ali, “Remote Sensing,” Laser Branch Department of Applied Sciences, University of Technology. Republic of Iraq, Ministry of Higher Education and Scientific Research. 2010.
    15. W. P. Menzel, “Remote sensing applications with meteorological satellites,” University of Wisconsin, Madison, WI. 2012.
    16. T. Zhang, W. Gong, W. Wang, Y. Ji, Z. Zhu, and Y. Huang, Ground Level PM2.5 estimates over China using satellite baed geographically weighted regression (GWR) models are improved by including NO2 and enhanced vegetation index (EVI),” International Journal of environment research and public health, vol. 13, pp. 1215, Dec 2016.
    17. G, Higgs, D. A. Sterling, S, AryL, A, Venulapalli, K. N, Priftis, and N. I. Sifakis, “Aerosol optical depth as a measure of particulate exposure using imputed censored data, and relationship with childhood asthma hospital admissions for 2004 in Athens, Greece,” Environmental Health Insight, vol. 9, pp. 27-33, April 2015.
    18. S.A. Ackerman, K.I. Strabala, W.P. Menzel, R.A. Frey, C.C. Moeller, and L.E. Gumley, “Discriminating clear sky from clouds with MODIS,” Journal of Geophysical Research, vol.103, pp. 139-140, Dec 1998.
    19. B.C. Gao, Y. J. Kaufman, D, Tanre, and R.R. Li, “Distinguishing tropospheric aerosols from thin cirrus clouds for improved aerosol retrievals using the ratio of 1.38- µm and 1.24 µm channels,” Geophysical Research Letters, vol. 29, pp. 1890, Sep 2002.
    20. J. V. Martins, D. Tanre, L.A. Remer, Y.J. Kaufman, S. Mattoo, and R. Levy, “MODIS cloud screening for remote sensing of aerosol over oceans using spatial variability,” Geophysical Research Letters, vol. 29, pp. 8009, Jun 2002.
    21. R.R. Li, Y.J. Kaufman, B.C. Gao, and C.O. Davis, “Remote sensing of suspended sediments and shallow coastal waters,” IEEE Transaction on Geoscience and Remote Sensing, vol. 41, pp. 559–566, March 2003.
    22. L. A. Remer, Y. J. Kaufman, D. Tanre, S. Mattoo, D. A. Chu, J. V. Martins, R. R. Li, C. Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. N. Holben, “The MODIS aerosol algorithm, products, and validation,” Journal of atmospheric Science, vol. 62, pp. 947–973, April 2005.
    23. D.G. Hadjimitsis, and C. Clayton, “Determination of aerosol optical thickness through the derivation of an atmospheric correction for shortwavelength Landsat TM and ASTER image data: an application to areas located in the vicinity of airports at UK and Cyprus,” Applied Geomatics, vol. 1, pp. 31-40, May 2009.
    24. H. Jethva, S. K. Satheesh, and J. Srinivasan, “Assessment of second-generation MODIS aerosol retrieval (Collection 005) at Kanpur, India,” Geophysical Research Letters, vol. 34, pp. L19802, Oct 2007.
    25. S. N. Tripathi, Sagnik Dey, A. Chandel, S. Srivastava, Ramesh P. Singh, and B. N. Holben, “Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India,” Annales Geophysices, vol. 23, pp. 1093-1101, Jun 2005.
    26. S. Chitranshi, S. P. Sharma, S. Dey, “Satellitebased estimates of outdoor particulate pollution (PM10) for Agra City in northern India,” Air Quality, Atmosphere & Health, vol. 8, pp. 55-65, Feb 2014.
    27. C. Lin, Y. Li, Z. Yuan, K. H. Alexis, l. C. Lau, and C. H. F. Jimmy, “Using satellite remote sensing data to estimate the high-resolution distribution of groundlevel PM2.5,” Remote Sensing of Environment, vol.156, pp. 117–128, Jan 2015.
    28. R.C. Levy, S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Has, “The Collection 6 MODIS aerosol products over land and ocean,” Atmospheric Measurement Techniques, vol. 6, pp. 2989–3034, Nov 2013.
    29. A. Retalis, and N. Sifakis, “Urban aerosol mapping over Athens using the differential textural analysis (DTA) algorithm on MERIS-ENVISAT data,” ISPRS Journal of Photogrammetry and remote sensing, vol. 65, pp. 17-25, Jan 2010.
    30. C. E. J. Tan, H. S. Lim, C. J. Wong, M. Z. MatJafri and K. Abdullah, "A Newly Computational Intelligence Algorithm for Air Quality Mapping Using SPOT Image over Penang, Malaysia," Fifth International Conference on Computer Graphics, Imaging and Visualisation, Penang, pp. 389-392, 2008.
    31. S.A.H. Saleh and G. Hasan, “Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image,” Journal of Remote Sensing and GIS, vol. 3, pp. 120, April 2014.
    32. M. Blackett, “Early Analysis of Landsat-8 Thermal Infrared Sensor Imagery of Volcanic Activity,” Remote Sensing, vol. 6, pp. 2282-2295, March 2014.
    33. R. K. Mishra, J. Pandey, S. K. Chaudhary, A. Khalkho, and V. K. Singh, “Estimation of air pollution concentration over Jharia coalfield based on satellite imagery of atmospheric aerosol,” International Journal of Geomatics and Geosciences, vol. 2, pp. 723-729, Feb 2012.
    34. D.G. Hadjimitsis, A. Retalis, and C.R.I. Clayton, “The assessment of atmospheric pollution using satellite remote sensing technology in large cities in the vicinity of airports,” Water, Air & Soil Pollution: Focus, vol. 2, pp. 631-640, Sep 2002.
    35. B.C. Forster, “Derivation of atmospheric correction procedures for Landsat MSS with particular reference to urban data,” International Journal of Remote Sensing, vol. 5, pp. 799-817, March1984.
    36. P. Techarat, “Mapping Predictive ambient concentration distribution of particulate matter and sulfur dioxide for air quality monitoring using remote sensing,” Doctorate Thesis, Dept. of Environment Study and Engineering, University of Regina, 2013.
    37. A. Roy, A. Jivani, B. Parekh, “Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data,” International Journal of Advanced Remote Sensing and GIS, vol. 6, pp. 2246-2252, July, 2017.

Recent Article