DOI QR코드

DOI QR Code

Detection of Yellow Sand Dust over Northeast Asia using Background Brightness Temperature Difference of Infrared Channels from MODIS

MODIS 적외채널 배경 밝기온도차를 이용한 동북아시아 황사 탐지

  • Park, Jusun (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University) ;
  • Kim, Jae Hwan (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University) ;
  • Hong, Sung Jae (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
  • 박주선 (부산대학교 지구환경시스템학부 대기과학전공) ;
  • 김재환 (부산대학교 지구환경시스템학부 대기과학전공) ;
  • 홍성재 (부산대학교 지구환경시스템학부 대기과학전공)
  • Received : 2011.11.02
  • Accepted : 2012.01.20
  • Published : 2012.06.30

Abstract

The technique of Brightness Temperature Difference (BTD) between 11 and $12{\mu}m$ separates yellow sand dust from clouds according to the difference in absorptive characteristics between the channels. However, this method causes consistent false alarms in many cases, especially over the desert. In order to reduce these false alarms, we should eliminate the background noise originated from surface. We adopted the Background BTD (BBTD), which stands for surface characteristics on clear sky condition without any dust or cloud. We took an average of brightness temperatures of 11 and $12{\mu}m$ channels during the previous 15 days from a target date and then calculated BTD of averaged ones to obtain decontaminated pixels from dust. After defining the BBTD, we subtracted this index from BTD for the Yellow Sand Index (YSI). In the previous study, this method was already verified using the geostationary satellite, MTSAT. In this study, we applied this to the polar orbiting satellite, MODIS, to detect yellow sand dust over Northeast Asia. Products of yellow sand dust from OMI and MTSAT were used to verify MODIS YSI. The coefficient of determination between MODIS YSI and MTSAT YSI was 0.61, and MODIS YSI and OMI AI was also 0.61. As a result of comparing two products, significantly enhanced signals of dust aerosols were detected by removing the false alarms over the desert. Furthermore, the discontinuity between land and ocean on BTD was removed. This was even effective on the case of fall. This study illustrates that the proposed algorithm can provide the reliable distribution of dust aerosols over the desert even at night.

Keywords

References

  1. 김학성, 정용승, 2009: 2005년 동아시아 지역에서 발생한 모래폭풍과 먼지침전(황사)의 관측. 한국지구과학회지, 30(2), 196-209.
  2. 신영철, 2006: 황사의 사회경제적 영향과 피해 비용. 설비저널, 35(4), 26-30.
  3. 이종재, 김철희, 2008: 최근의 황사 발원지에서의 먼지 발생 특성-2002년 이후 먼지발생 경향 분석. 대기, 18(4), 493-506.
  4. 조창범, 전영신, 구본양, 박순웅, 이상삼, 정연앙, 2007: 황사농도 단기예측모델의 PM10 농도와 실측 PM10 농도의 비교-2006년 4월 7-9일 황사 현상에 대해. 대기, 17(1), 87-99.
  5. 하종성, 김재환, 이현진, 2006: 적외선 채널을 이용한 에어로솔 탐지의 경계값 및 민감도 분석. 대한원격탐사학회지, 22(6), 507-518.
  6. 홍성재, 김재환, 하종성, 2010: 봄철 황사탐지를 위한 정지궤도위성 적외선 채널의 배경경계값 적용 가능성 연구. 대한원격탐사학회지, 26(4), 387-394. https://doi.org/10.7780/kjrs.2010.26.4.387
  7. Ackerman, S. A., 1989: Using the radiative temperature difference at 3.7 and $11{\mu}m$ to tract dust outbreaks. Remote Sensing of Environment, 27(2), 129-133. https://doi.org/10.1016/0034-4257(89)90012-6
  8. Ackerman, S. A., 1997: Remote sensing aerosols using satellite infrared observations. Journal of Geophysical Research, 102(D14), 17069-17079. https://doi.org/10.1029/96JD03066
  9. Ackerman, S., R. Frey, K. Strabala, Y. Liu, L. Gumley, B. Baum, and P. Menzel, 2010: Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document(MOD35). MODIS Cloud Mask Team, 6.1, 117pp.
  10. Ellrod, G. P., B. H. Connell, and D. W. Hillger, 2003: Improved detection of airborne volcanic ash using multispectral infrared satellite data. Journal of Geophysical Research, 108(D12), 4356. https://doi.org/10.1029/2002JD002802
  11. Legrand, M., A. Plana-Fattori, and C. N'doume, 2001: Satellite detection of dust using the IR imagery of Meteosat 1. Infrared difference dust index. Journal of Geophysical Research, 106(18), 18251-18274. https://doi.org/10.1029/2000JD900749
  12. Prata, A. J., 1989: Observations of volcanic ash clouds in the $10-12{\mu}m$ window using AVHRR/2 data. International Journal of Remote Sensing, 10(4-5), 751-761. https://doi.org/10.1080/01431168908903916
  13. Stammes, P., and R. Noordhoek, 2002: OMI Algorithm theoretical basis document: Clouds, aerosols, and surface UV irradiance. OMI Team, 3, 114.
  14. Torres, O., P. K. Bhartia, J. R. Herman, Z. Ahmad, and J. Gleason, 1998: Derivation of aerosol properties from satellite measurements of backscatterd ultraviolet radiation: Theoretical basis. Journal of Geophysical Research, 103(D14), 17099-17110. https://doi.org/10.1029/98JD00900
  15. Torres, O., A. Tanskanen, B. Veihelmann, C. Ahn, R. Braak, P. K. Bhartia, P. Veefkind, and P. Levelt, 2007: Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview. Journal of Geophysical Research, 112(D24), doi: 10.1029/2007JD008809.
  16. Wen, S., and W. Rose, 1994: Retrieval of sizes and total masses of particles in volcanic clouds using AVHRR bands 4 and 5. Journal of Geophysical Research, 99(D3), 5421-5431. https://doi.org/10.1029/93JD03340
  17. Zhai, P., and R. E. Eskridge, 1997: Atmospheric Water Vapor over China. Journal of Climate, 10, 2643-2652. https://doi.org/10.1175/1520-0442(1997)010<2643:AWVOC>2.0.CO;2

Cited by

  1. Aerosol Size Distributions and Optical Properties during Severe Asian Dust Episodes Measured over South Korea in Spring of 2009-2010 vol.22, pp.3, 2012, https://doi.org/10.14191/Atmos.2012.22.3.367
  2. Derivation of Geostationary Satellite Based Background Temperature and Its Validation with Ground Observation and Geographic Information vol.31, pp.6, 2015, https://doi.org/10.7780/kjrs.2015.31.6.8