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Correlation Analysis between Terra/Aqua MODIS LST and Air Temperature: Mainly on the Occurrence Period of Heat and Cold Waves

Terra/Aqua MODIS LST와 기온과의 상관성 분석: 한파 및 폭염 발생 기간을 중심으로

  • CHUNG, Jee-Hun (Dept. of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • LEE, Yong-Gwan (Dept. of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • LEE, Ji-Wan (Dept. of Civil, Environmental and Plant Engineering, Konkuk University) ;
  • KIM, Seong-Joon (School of Civil and Environmental Engineering, Konkuk University)
  • 정지훈 (건국대학교 사회환경플랜트공학과) ;
  • 이용관 (건국대학교 사회환경플랜트공학과) ;
  • 이지완 (건국대학교 사회환경플랜트공학과) ;
  • 김성준 (건국대학교 사회환경공학부)
  • Received : 2019.09.19
  • Accepted : 2019.12.21
  • Published : 2019.12.31

Abstract

In this study, the correlation analysis was conducted between observed air temperature (maximum, minimum, and mean air temperature) and the daytime and nighttime data of Terra/Aqua MODIS LST(Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) for 86 weather stations. All the data of the recent 11 years from 2008 to 2018 were prepared with daily base. In particular, the characteristics of the cold and heat waves incidence period in 2018 were analyzed. The correlation analysis was performed using the Pearson correlation coefficient(R) and root mean square error(RMSE). As a result of time series analysis, the trend between observed air temperature and MODIS LST were similar, showing the correlation above 0.9 in maximum temperature, above 0.8 in mean and minimum temperature. Especially, the maximum temperature was found to have the highest accuracy with Terra MODIS LST daytime, and the minimum temperature had the highest correlation with Terra MODIS LST nighttime. During the cold wave period, both Terra and Aqua MODIS LST showed higher correlations with nighttime data than daytime data. For the heat wave period, the Aqua MODIS LST daytime data was good, but the overall R was below 0.5. Additional analysis is necessary for further study considering such as land cover and elevation characteristics.

본 연구에서는 Terra/Aqua MODIS LST(Moderate Resolution Imaging Spectroradiometer Land Surface Temperature)의 Daytime, Nighttime 자료와 기상청 기상관측소 86개 지점에 대한 최고, 최저 및 평균기온을 이용하여 두 자료 사이의 상관성을 분석하고, 한파 및 폭염 발생 기간의 특성을 집중적으로 분석하였다. 모든 자료는 2008년부터 2018년까지 총 11년간 일별로 구축하였으며, Pearson 상관계수(Pearson correlation coefficient, R)와 평균제곱근오차(Root Mean Square Error, RMSE)를 이용하여 상관성 분석을 수행하였다. 시계열 분석 결과, 대상 기간 전체에서 기온과 MODIS LST 간의 변동 양상은 유사하였고, 최고 기온과 MODIS 자료의 R 0.9 이상, 평균기온과 최저 기온과는 0.8 이상으로 기온과 MODIS LST 사이의 상관성은 높은 것으로 나타났다. 특히, 최고 기온은 Terra MODIS LST Daytime과 정확도가 제일 높고, 최저 기온은 Terra MODIS LST Nighttime과 상관성이 제일 높은 것으로 분석되었다. 한파 기간에는 Terra/Aqua MODIS 모두 주간 자료보다 야간 자료의 상관성이 더 높은 것으로 분석되었으며, 특히 Terra MODIS LST Nighttime과의 상관성이 좋은 것으로 분석되었다. 폭염 기간에는 Aqua MODIS LST Daytime 자료가 가장 좋은 것으로 분석되었으나, 전체적인 R이 0.5보다 낮아 추후 활용을 위해서는 식생이나 토지이용, 고도 등 다른 요소를 활용한 추가 분석이 필요할 것으로 판단된다.

Keywords

References

  1. Bae, D.H., H.M. Kim, S.R. Ha. 2018. The factor analysis of Land Surface Temperature(LST) change using MODIS imagery and panel data. Journal of the Korean Association of Geographic Information Studies. 21(1):46-56 https://doi.org/10.11108/KAGIS.2018.21.1.046
  2. Baek, J.J. and M.H. Choi. 2012. Availability of Land Surface Temperature from the COMS in the Korea Peninsula. Journal of Korea Water Resources Association. 45(8):755-765 https://doi.org/10.3741/JKWRA.2012.45.8.755
  3. Baek, J.J. and M.H. Choi. 2015. Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea. International Journal of Remote Sensing. 36(1):375-402. https://doi.org/10.1080/01431161.2014.998349
  4. Becker F. 1987. The impact of spectral emissivity on the measurement of land surface temperature from a satellite. Remote Sensing. 8(10):1509-1522. https://doi.org/10.1080/01431168708954793
  5. Benali A., A.C. Carvalho, J.P. Nunes, N. Carvalhais, and A. Santos. 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment. 124(1):108-121. https://doi.org/10.1016/j.rse.2012.04.024
  6. Byun, M.J., K.S. Han, and Y.S. Kim. 2004. A new look at the statistical method for remote sensing of daily maximum air temperature. Korean Journal of Remote Sensing. 20(2):65-76 https://doi.org/10.7780/kjrs.2004.20.2.65
  7. Colombi A., C.D. Michele, M. Pepe, and A. Rampini. 2007. Estimation of daliy mean air temperature from MODIS LST in Alpine areas. EARSeL eProceedings. 6(1):2007.
  8. Duan S.B., Z.L. Li, H. Li, F.M. Gottsche, H. Wu, W. Zhao, P. Leng, X. Zhang, and C. Coll. 2019. Validation of Collection 6 MODIS land surface temperature product using in situ measurement. Remote Sensing of Environment. 225:16-29. https://doi.org/10.1016/j.rse.2019.02.020
  9. Hong, H.C., W.J. Kim, J.Y. Kim, and B.J. Kim. 2013. Analysis of demand characteristics for long-term forecasts. Journal of Climate Research. 8(2):117-126 https://doi.org/10.14383/cri.2013.8.2.117
  10. IPCC(Intergovernmental Panel on Climate Change). 2007. Climate change 2007: The physical science basis, IPCC contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. p.996.
  11. Jee, J.B., K.T. Lee, and Y.J. Choi. 2014. Analysis of Land Surface Temperature from MODIS and Landsat satellites using by AWS temperature in capital area. Korean Journal of Remote Sensing. 30(2):315-329 https://doi.org/10.7780/kjrs.2014.30.2.13
  12. Jeon, M.J., and Y.S. Cho. 2015. An analysis of a winter-time temperature change and an extreme cold waves frequency in Korea. Journal of Climate Change Research. 6(2):87-94 https://doi.org/10.15531/ksccr.2015.6.2.87
  13. Jo, M.H., K.J. Lee, and W.S. Kim. 2001. A study on the spatial distribution characteristic of urban surface temperature using remotely sensed data and GIS. Journal of the Korean Association of Geographic Information Studies. 4(1):57-66
  14. Kim, D.Y. 2015a. Development of estimation algorithm of near-surface air temperature for warm and cold seasons in Korea. Journal of the Korean Society for Geospatial Information Science. 23(4):11-16 https://doi.org/10.7319/kogsis.2015.23.4.011
  15. Kim, D.Y. 2015b. Development of statistical estimation model for seasonal air temperature over Korea. Journal of Korean Society of Environment Technology. 16(5):369-375
  16. KMA(Korea Meteorological Administration). 2018. Comparison of heat waves between 2018 and 1994. p.4-8
  17. KMA(Korea Meteorological Administration). 2019. Weather characteristics in 2018
  18. Lakshmi V., K. Czajkowski, R. Dubayah, and J. Susskind. 2001. Land surface air temperature mapping using TOVS and AVHRR. International Journal of Remote Sensing. 22(4):643-662. https://doi.org/10.1080/01431160050505900
  19. Lee, C.S., K.S. Han, J.M. Yeom, B.G. Song, and Y.S. Kim. 2007. Thermal spatial representativity of meteorological stations using MODIS Land Surface Temperature. Journal of the Korean Association of Geographic Information Studies. 10(3):123-133
  20. Lee, H.M., H.C. Jung, J.E. Wie, and B.K. Moon. 2018. Climate over the Korean Peninsula: Heat wave, cold wave, drought, and ocean warming. Journal of Science and Science Education. 43(1):13-22
  21. Lee, N.Y., and Y.S. Cho. 2015. Estimation of the medical costs incurred by the elderly in Korea due to heat waves and analysis of the causes for expenditure. Journal of Environmental Policy and Administration. 23(2):153-172 https://doi.org/10.15301/jepa.2015.23.2.153
  22. Lee, S.H., J.S. Ahn, H.D. Kim, and S.J. Hwang. 2009. Comparison study on the estimation algorithm of Land Surface Temperature for MODIS data at the Korean Peninsula. Journal of the Environmental Sciences. 18(4):355-367 https://doi.org/10.5322/JES.2009.18.4.355
  23. Lee, Y.G., S.J. Kim. 2016. The modified SEBAL for mapping daily spatial evapotranspiration of South Korea using three flux towers and Terra MODIS data. Remote Sensing. 8(12):983. https://doi.org/10.3390/rs8120983
  24. Lee, Y.G., S.H. Kim, S.R. Ahn, M.H. Choi, K.S. Lim, and S.J. Kim. 2015. Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model - A Case of Yongdam dam watershed -. Journal of the Korean Association of Geographic Information Studies. 18(1):90-104 https://doi.org/10.11108/kagis.2015.18.1.090
  25. Mcmillin, L.M. 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. Oceans. 80(36):5113-5117. https://doi.org/10.1029/JC080i036p05113
  26. Min, J.S., M.H. Lee, J.B. Jee, and M. Jang. 2016. A study of the method for estimating the missing data from weather measurement instrument. Journal of Digital Convergence. 14(8):245-252 https://doi.org/10.14400/JDC.2016.14.8.245
  27. Moriasi, D.N., J.G. Arnold, M.W. Van Liew, R.L. Binger, R.D. Harmel, and T.L. Veith, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 50(3):885-900. https://doi.org/10.13031/2013.23153
  28. NEMA(National Emergency Management Agency). 2013. Natural disaster yearbook. p.33-34
  29. Neteler, M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sensing. 2(1):333-351. https://doi.org/10.3390/rs1020333
  30. NIMR(National Institute of Meteorological Sciences). 2018. Climate change over 100 years on the Korean Peninsula.
  31. Park, S.Y. 2009. Estimating air temperature over mountainous terrain by combining hypertemporal satellite LST data and multivariate geostatistical methods. Journal of the Korean Geographical Society. 44(2):105-121
  32. Price, J.C. 1984. Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal of Geophysical Research: Atmospheres. 89(5):7231-7237. https://doi.org/10.1029/JD089iD05p07231
  33. Park, K.H., B.G. Song, and J.E. Park. 2016. Analysis on the effects of land cover types and topographic features on heat wave days. Journal of the Korean Association of Geographic Information Studies. 19(4):76-91 https://doi.org/10.11108/kagis.2016.19.4.076
  34. Prihodko L., and S.N. Goward. 1997. Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment. 60(3):335-346. https://doi.org/10.1016/S0034-4257(96)00216-7
  35. Seguin, B. 1991. Use of surface temperature in agrometeorology. Applications of Remote Sensing to Agrometeorology. p.221-240.
  36. Shin, H.S., E.M. Chang, and S.W. Hong. 2014. Estimation of near surface air temperature using MODIS Land Surface Temperature data and geostatistics. Journal of Korea Spatial Information Society. 22(1):55-63
  37. Suga, Y., H. Ogawa, K. Ohno, and K. Yamada. 2003. Detection of surface temperature from Landsat-7/ETM+. Advances in Space Research. 32(11):2235-2240. https://doi.org/10.1016/S0273-1177(03)90548-5
  38. Suh, E.H. 2018. IBM SPSS Statistics. Free Academy Inc., South Korea, p.203-204
  39. Snyder, W.C., Z. Wan, Y. Zhang, and Y.Z. Feng. 1998. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing. 19(14):2753-2774. https://doi.org/10.1080/014311698214497
  40. Vancutsem, C., P., Ceccato, T., Dinku, and S.J., Connor. 2010. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment. 114(2):449-465. https://doi.org/10.1016/j.rse.2009.10.002
  41. Wan, Z., and J. Dozier. 1996. A generalized split-window algorithm for retrieving Land-Surface Temperature from space. IEEE Transactions on Geoscience and Remote Sensing. 34(4):892-905. https://doi.org/10.1109/36.508406
  42. Wan, Z. 1997. Land Surface Temperature measurements from EOS MODIS data.
  43. Wan, Z. 1999. MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD). p.13-17.
  44. Yan, H., J. Zhang, Y. Hou, and Y. He. 2009. Estimation of air temperature from MODIS data in east China. International Journal of Remote Sensing. 30(23):6261-6275. https://doi.org/10.1080/01431160902842375
  45. Yang, Y.Z., W.H. Cai, and J. Yang. 2017. Evaluation of MODIS Land Surface Temperature data to estimate nearsurface air temperature in northeast China. Remote Sensing. 9(5):410. https://doi.org/10.3390/rs9050410
  46. Yoo, C.H., J.H. Im, S.Y. Park, and L.J. Quackenbush. 2018. Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS Journal of Photogrammetry and Remote Sensing. 137(1):149-162 https://doi.org/10.1016/j.isprsjprs.2018.01.018
  47. Yoon, M.H., and T.M. Ahn. 2009. An application of satellite image analysis to visualize the effects of urban green areas on temperature. Journal of the Korean Institute of Landscape Architecture. 37(3):46-53
  48. Zeng, L., B.D. Wardlow, T. Tadesse, J. Shan, M.J. Hayes, D. Li, and D. Xiang. 2015. Estimation of daily air temperature based on MODIS Land Surface Temperature products over the corn belt in the US. Remote Sensing. 7(1):951-970. https://doi.org/10.3390/rs70100951