Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Retrieval of Atmospheric Optical Thickness from Digital Images of the Moon

월면 디지털 영상 분석을 이용한 대기 광학두께 산출

  • Jeong, Myeong-Jae (Department of Atmospheric and Environmental Sciences/Natural Science Research Institute, Gangneung-Wonju National University)
  • 정명재 (강릉원주대학교 대기환경과학과/자연과학연구소)
  • Received : 2013.09.14
  • Accepted : 2013.10.21
  • Published : 2013.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Atmospheric optical thickness during nighttime was estimated in this study using analysis on the images of the moon taken from commercial digital camera. Basically the Langely Regression method was applied to the observations of the moon for the cloudless and optically stable sky conditions. The spectral response functions for the red(R), green(G), and blue(B) channels were employed to derive effective wavelength centers of each channel for the observations of the moon, and the correspondent Rayleigh optical thickness were also calculated. Aerosol optical thickness (AOT) was calculated by subtracting Rayleigh optical thickness from the atmospheric optical thickness derived from the Langley regression method. As there are only handful of nighttime AOT observations, the AOT from the moon observations was compared with the AOT from sun-photometers and the MODIS satellite sensor, which was taken several hours before the moon observations of this study. As a result, the values of AOT from moon observations agree with those from sun-photometers and MODIS within 0.1 for the R, G, B channels of the digital camera. On the other hand, ${\AA}$ngstr$\ddot{o}$m Exponent seems to be subject to larger errors due to its sensitiveness to the spectral errors of AOT. Nevertheless, the results of this study indicate that the method reported in this study is promising as it can provide nighttime AOT relatively easily with a low cost instrument like digital camera. More observations and analyses are warranted to attain improved nighttime AOT observations in the future.

이 연구에서는 상용 디지털 카메라를 이용하여 야간에 촬영된 월면 영상을 분석하여 야간의 대기 광학두께와 에어로솔 광학두께를 추정하였다. 기본적으로 랑리회귀법을 이용하였으며 구름이 없고 대기의 광학적 특성이 비교적 안정한 날에 관측을 수행하였다. 카메라의 적색(R), 녹색(G), 청색(B) 채널의 파장별 반응함수를 이용하여 월광관측에 대한 유효 중심파장 및 레일리 광학두께를 추정하였으며, 랑리 회귀법에서 유도된 대기광학두께로부터 레일리 광학두께를 제하여 에어로솔 광학 두께를 산출하였다. 야간에는 독립적인 방법으로 산출된 검증자료나 다른 에어로솔 광학두께 자료가 거의 없으므로 월면 관측이 이루어지기 수 시간 전의 주간에 정밀한 태양분광광도계로 측정된 에어로솔 광학두께 자료와 MODIS 위성센서 관측으로부터 산출된 에어로솔 광학두께 자료를 본 연구에서 월면 관측을 통해 산출된 자료와 비교하였다. 비교 결과 R, G, B 채널에서 대략 0.1정도의 오차 범위에서 월면 영상분석을 통해 에어로솔 광학두께의 추정이 가능함을 알 수 있었다. 단, 대기 중의 에어로솔 입자들의 크기를 나타내는 모수인 앙스트롬지수(${\AA}$ngstr$\ddot{o}$m Exponent)는 파장별 광학두께의 작은 오차에도 큰 오차를 가질 수 있기 때문에 에어로솔 광학두께의 오차에 비해 비교적 큰 오차를 보일 수 있음이 나타났다. 그럼에도 불구하고, 야간의 에어로솔 광학두께 자료가 많지 않은 현실에서 저비용으로 월면 관측을 통하여 에어로솔 광학두께를 산출할 수 있는 가능성을 찾았다는 점에서 본 연구의 의의가 있으며 앞으로 보다 많은 관측과 분석을 통해 보다 향상된 야간 에어로솔 광학두께 추정이 가능할 것으로 보인다.

Keywords

References

  1. Baumer, D., S. Versick, B. Vogel, 2008. Determination of the visibility using a digital panorama camera, Atmospheric Environment, 42: 2593-2602, doi:10.1016/j.atmosenv.2007.06.024.
  2. Berkoff, Timothy A., Mikail Sorokin, Tom Stone, Thomas F. Eck, Raymond Hoff, Ellsworth Welton, Brent Holben, 2011. Nocturnal Aerosol Optical Depth Measurements with a Small- Aperture Automated Photometer Using the Moon as a Light Source, Journal of Atmospheric and Oceanic Technology, 28: 1297-1306. https://doi.org/10.1175/JTECH-D-10-05036.1
  3. Bucholtz, A., 1995. Rayleigh-scattering calculations for the terrestrial atmosphere, Applied Optics, 34:2765-2773. https://doi.org/10.1364/AO.34.002765
  4. Cho, H.K., M.-J. Jeong, J. Kim, and Y.J. Kim, 2003. Dependence of diffuse photosynthetically active solar irradiance on total optical depth, Journal of Geophysical Research, 108(D9), 4267, doi:10.1029/2002JD002175.
  5. Eck, T.F., B.N. Holben, J.S. Reid, O. Dubovik, A. Smirnov, N.T. O'Neill, I. Slutsker, and S. Kinne, 1999. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, Journal of Geophysical Research, 104: 31,333-31,349. https://doi.org/10.1029/1999JD900923
  6. Fischer, E.V., N.C. Hsu, D.A. Jaffe, M.-J. Jeong, and S.L. Gong, 2009. A decade of dust: Asian dust and springtime aerosol load in the U.S. Pacific Northwest, Geophysical Research Letters, 36, L03821, doi:10.1029/2008GL036467.
  7. Harrison, L., J. Michalsky, and J. Berndt, 1994. Automated multifilter rotating shadow-band radiometer: An instrument for optical depth and radiation measurement, Applied Optics, 33:5118-5125. https://doi.org/10.1364/AO.33.005118
  8. Herber, A., L.W. Thomason, H. Gernandt, U. Leiterer, D. Nagel, K-H Schulz, J. Kaptur, T. Albrecht, and J. Notholt, 2002. Continuous day and night aerosoloptical depth observations in the Arctic between 1991 and 1999. Journal of Geophysical Research, 107, 4097, doi:10.1029/2001JD000536.
  9. Holben, B.N., T.F. Eck, I. Slutsker, D. Tanre, J.P. Buis, A. Setzer, E. Vermote, J.A. Reagan, Y.J. Kaufman, T. Nakajima, F. Lacenu, I. Jankowiak, and A. Smirnov, 1998. AERONET_A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66: 1-16. https://doi.org/10.1016/S0034-4257(98)00031-5
  10. Hsu, N.C., M.-J. Jeong, C. Bettenhausen, A.M. Sayer, R. Hansell, C. Seftor, J. Huang, and S.-C. Tsay, 2013. Enhanced Deep Blue Aerosol Retrieval Algorithm: The second Generation, Journal of Geophysical Research, 118, doi:10.1002/jgrd.50712.
  11. Intergovernmental Panel on Climate Change (IPCC), 2001. Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  12. Intergovernmental Panel on Climate Change (IPCC), 2007. Climate change 2007: the scientific basis, In: Solomon, S. (Ed.), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, New York.
  13. Iqbal, M., 1983. An Introduction to Solar Radiation, Academic Press, San Diego, CA, USA.
  14. Jeong, M.-J. and Z. Li, 2005. Quality, compatibility and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and total ozone mapping spectrometer, Journal of Geophysical Research, 110, D10S08, doi:10.1029/2004JD004647.
  15. Jeong, M.-J., and N.C. Hsu, 2008. Retrievals of aerosol single-scattering albedo and effective aerosol layer height for biomass-burning smoke: Synergy derived from "A-Train" sensors, Geophysical Research Letters, 35, L24801, doi:10.1029/2008GL036279.
  16. Kasten, F., 1966. A new table and approximate formula for relative optical air mass, Arch. Meteorol. Geophys. Bioklimatol., Ser. B, 14: 206-223.
  17. Kaufman, Y.J., D. Tanré, and O. Boucher, 2002. A satellite view of aerosols in the climate system, Nature, 419: 215-223. https://doi.org/10.1038/nature01091
  18. Kieffer, H.H. and T.C. Stone, 2005. The spectral irradiance of the moon, The Astronomical Journal, 129: 2887-2901. https://doi.org/10.1086/430185
  19. Kim, K.W. and Y.J. Kim, 2005. Perceived visibility measurement using the HSI color difference method, J. Korean Physical Society, 46(5):1243-1250.
  20. Kim, K.W. and Y.J. Kim, 2002. Visibility observations using remote digital vision visibility monitor, Atmosphere, 12(3): 80-81.
  21. King, M.D., Y. Kaufman, D. Tanre, and T. Nakajima, 1999. Remote sensing of tropospheric aerosols from space: Past, present, and future, Bulletine of American Meteorological Society, 11: 2229-2259.
  22. Lebourgeois, V., A. Begue, S. Labbe, B. Mallavan, L. Prevot, and B. Roux, 2008. Can Commercial Digital Cameras Be Used as Multispectral Sensors- A Crop Monitoring Test, Sensors, 8: 7300-7322, doi:10.3390/s8117300.
  23. Leckner, B. 1978. The spectral distribution of solar radiation at the earth's surface - elements of a model, Solar Energy, 20(2): 143-150. https://doi.org/10.1016/0038-092X(78)90187-1
  24. Lee, J.H., J. Kim, C.H. Song, J.-H. Ryu, Y.-H. Ahn, and C.K. Song, 2010. Algorithm for Retrieval of Aerosol Optical Properties over the Ocean from the Geostationary Ocean Color Imager, Remote Sensing of Environment, 114: 1077-1088, 10.1016/j.rse.2009.12.021.
  25. Lee, S.-S., and G. Feingold, 2010. Precipitating cloudsystem response to aerosol perturbations, Geophysical Research Letters, 37, doi:10.1029/2010GL045596.
  26. Levy, R.C., L.A. Remer, S. Mattoo, E.F. Vermote, and Y.J. Kaufman, 2007. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, Journal of Geophysical Research, 112(D13211), doi:10.1029/2006JD007811.
  27. Noh, Y. and K.-H. Lee, 2013. Characterization of Optical Properties of Long-range Transported Asian Dust in NorthEast Asia, Korean J. of Remote Sensing, 29(2): 243-251 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2013.29.2.8
  28. Perez-Ramirez, D., J. Aceituno, B. Ruiz, F.J. Olmo, L. Alados-Arboledas, 2008. Development and calibration of a star photometer to measure the aerosol optical depth Smoke observations at a high mountain site. Atmospheric Environment, 42(11): 2733-2738. https://doi.org/10.1016/j.atmosenv.2007.06.009
  29. Remer, L.A., 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, 2005. The MODIS aerosol algorithm, products, and validation, J. Atmospheric Science, 62(4): 947-973. https://doi.org/10.1175/JAS3385.1
  30. Robinson, N. (ed.), 1966. Solar Radiation, American Elsevier, New York.
  31. Seinfeld, J.H. and S.N. Pandis, 2006. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed. Wiley-Interscience, New Jersey, p1232.
  32. Seo, S.-B. and K.-W. Jin, 2013. Degradation Monitoring of Visible Channel Detectors on COMS MI Using Moon Observation Images, Korean J. of Remote Sensing, 29(1): 115-121 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2013.29.1.11
  33. Smirnov, A., B.N. Holben, I. Slutsker, D.M. Giles, C.R. McClain, T.F. Eck, S.M. Sakerin, A. Macke, P. Croot, G. Zibordi, P.K. Quinn, J. Sciare, S. Kinne, M. Harvey, T.J. Smyth, S. Piketh, T. Zielinski, A. Proshutinsky, J.I. Goes, N.B. Nelson, P. Larouche, V.F. Radionov, P. Goloub, K. Krishna Moorthy, R. Matarrese, E.J. Robertson, and F. Jourdin, 2009. Maritime Aerosol Network as a component of Aerosol Robotic Network, Journal of Geophysical Research, 114, D06204, doi:10.1029/2008JD011257.
  34. Spigulis, J.D. Jakovels, and U. Rubins, 2010. Multispectral skin imaging by a consumer photocamera, Multimodal Biomedical Imaging V, edited by Fred S. Azar, Xavier Intes, Proc. of SPIE, 7557(75570M): 1-9, doi:10.1117/12.845492.
  35. United States Naval Observatory, 2012. Multiyear Interactive Computer Almanac 1800-2050, Version 2.2.2. Willmann-Bell, Inc., Richmond, VA, USA.
  36. Xie, L., A. Chiu, and S. Newsam, 2008. Estimating atmospheric visibility using general-purpose cameras, G. Bebis et al.(Eds.): ISVC 2008, Part II, LNCS 5359, Springer-Verlag, Berlin/Heidelberg: pp. 356-367.

Cited by

  1. Retrieval of Nighttime Aerosol Optical Thickness from Star Photometry vol.25, pp.3, 2015, https://doi.org/10.14191/Atmos.2015.25.3.521
  2. Estimation of nighttime aerosol optical thickness from Suomi-NPP DNB observations over small cities in Korea vol.32, pp.2, 2016, https://doi.org/10.7780/kjrs.2016.32.2.1