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http://dx.doi.org/10.11108/kagis.2019.22.4.197

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

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)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.22, no.4, 2019 , pp. 197-214 More about this Journal
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.
Keywords
LST; Terra/Aqua MODIS; Air Temperature; Cold Wave; Heat Wave;
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1 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   DOI
2 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   DOI
3 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.   DOI
4 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   DOI
5 Mcmillin, L.M. 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. Oceans. 80(36):5113-5117.   DOI
6 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   DOI
7 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.   DOI
8 NEMA(National Emergency Management Agency). 2013. Natural disaster yearbook. p.33-34
9 Neteler, M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sensing. 2(1):333-351.   DOI
10 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
11 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.   DOI
12 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   DOI
13 Prihodko L., and S.N. Goward. 1997. Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment. 60(3):335-346.   DOI
14 Suh, E.H. 2018. IBM SPSS Statistics. Free Academy Inc., South Korea, p.203-204
15 Seguin, B. 1991. Use of surface temperature in agrometeorology. Applications of Remote Sensing to Agrometeorology. p.221-240.
16 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
17 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.   DOI
18 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.   DOI
19 NIMR(National Institute of Meteorological Sciences). 2018. Climate change over 100 years on the Korean Peninsula.
20 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   DOI
21 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   DOI
22 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.   DOI
23 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.   DOI
24 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.   DOI
25 Wan, Z. 1997. Land Surface Temperature measurements from EOS MODIS data.
26 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.   DOI
27 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.   DOI
28 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   DOI
29 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
30 Wan, Z. 1999. MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD). p.13-17.
31 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   DOI
32 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.   DOI
33 Becker F. 1987. The impact of spectral emissivity on the measurement of land surface temperature from a satellite. Remote Sensing. 8(10):1509-1522.   DOI
34 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.   DOI
35 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.
36 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.   DOI
37 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   DOI
38 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   DOI
39 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.
40 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   DOI
41 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
42 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   DOI
43 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
44 KMA(Korea Meteorological Administration). 2018. Comparison of heat waves between 2018 and 1994. p.4-8
45 KMA(Korea Meteorological Administration). 2019. Weather characteristics in 2018
46 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.   DOI
47 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
48 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