JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Derivation of Geostationary Satellite Based Background Temperature and Its Validation with Ground Observation and Geographic Information
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 6,  2015, pp.583-598
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.6.8
 Title & Authors
Derivation of Geostationary Satellite Based Background Temperature and Its Validation with Ground Observation and Geographic Information
Choi, Dae Sung; Kim, Jae Hwan; Park, Hyungmin;
  PDF(new window)
 Abstract
This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.
 Keywords
Brightness temperature;Cloud filtering;CMDPS;Land surface temperature;Land cover;
 Language
Korean
 Cited by
 References
1.
Aduah, M.S., S. Mantey, and N.D. Tagoe, 2012. Mapping land surface temperature and land cover to detect urban heat island effect: a case study of Tarkwa, South West Ghana, Research Journal of Environmental and Earth Sciences, 4(1): 68-75.

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 (in Korean with English abstract). crossref(new window)

3.
Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes, and A.A.M. Holtslag, 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, Journal of hydrology, 212(213): 198-212.

4.
Carlson, T.N. and S.T. Arthur, 2000. The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective, Global and Planetary Change, 25(1): 49-65. crossref(new window)

5.
Channan, S.K., K. Collins, and W.R. Emanuel, 2014. Global mosaics of the standard MODIS land cover type data, University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA.

6.
Chase, T.N., R.A. Pielke Sr, T.G.F. Kittel, R.R. Nemani, and S.W. Running, 2000. Simulated impacts of historical land cover changes on global climate in northern winter, Climate Dynamics, 16(2-3): 93-105. crossref(new window)

7.
Chen, X.-L., H.-M. Zhao, P.-X. Li, and Z.-Y. Yin, 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote sensing of environment, 104(2): 133-146. crossref(new window)

8.
Cho, A.-R. and M.-S. Suh, 2013. Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data, Remote Sensing, 5(8): 3951-3970. crossref(new window)

9.
Choi, Y.-Y., M.-S. Suh, and K.-H. Park, 2014. Assessment of surface urban heat islands over three megacities in East Asia using land surface temperature data retrieved from COMS, Remote Sensing, 6(6): 5852-5867. crossref(new window)

10.
Chung, C.Y., H.K. Lee, H.J. Ahn, M.H. Ahn, and S.N. Oh, 2006. Developing the Cloud Detection Algorithm for COMS Meteorolgical Data Processing System, Korean Journal of Remote Sensing, 22(5): 367-372.

11.
Eidenshink, J.C. and J.L. Faundeen, 1994. The 1 km AVHRR global land data set: first stages in implementation, International Journal of Remote Sensing, 15(17): 3443-3462. crossref(new window)

12.
Feng, X. and H. Shi, 2011. Pattern of urban heat island in Xi'an by remote sensing image, Proc. of 2011 International Symposium on Water Resource and Environmental Protection (ISWREP), vol. 3, pp. 2399-2401.

13.
Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sensing of Environment, 114(1): 168-182. crossref(new window)

14.
Gao, X., A.R. Huete, W. Ni, and T. Miura, 2000. Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination, Remote Sensing of Environment, 74(3): 609-620. crossref(new window)

15.
Goward, S.N., S. Turner, D.G. Dye, and S. Liang, 1994. The University of Maryland improved global vegetation index product, International Journal of Remote Sensing, 15(17): 3365-3395. crossref(new window)

16.
Han, H., N. Guo, D. Cai, and J. Wang, 2011. FY-2D retrieved surface temperature change as a predictor for sandstorm forecasting over Northwest China, Proc. of Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, jul. 24-29, pp. 3261-3264.

17.
Harries, J.E., J.E. Russell, J.A. Hanafin, H. Brindley, J. Futyan, J. Rufus, and M.A. Ringer, 2005. The Geostationary Earth Radiation Budget Project, Bulletin of the American Meteorological Society, 86(7): 945-960. crossref(new window)

18.
Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data, International Journal of Remote Sensing, 7(11): 1417-1434. crossref(new window)

19.
Hong, K.-O., M.-S. Suh, and J.-H. Kang, 2009. Development of a Land Surface Temperature-Retrieval Algorithm from MTSAT-1R Data, Asia-Pacific Journal of Atmospheric Sciences, 45(4): 411-421.

20.
Hong, S.J., J.H. Kim, and J.S. Ha, 2010. Possibility of Applying Infrared Background Threshold Values for Detecting Asian dust in Spring from Geostationary Satellite, Korean Journal of Remote Sensing, 26(4): 387-394 (in Korean with English abstract).

21.
Huete, A., C. Justice, and Leeuwen, W. van, 1999. MODIS VEGETATION INDEX(MOD 13), ALGORITHM THEORETICAL BASIS DOCUMENT, 3: 213.

22.
Kalnay, E. and M. Cai, 2003. Impact of urbanization and land-use change on climate, Nature, 423(6939): 528-531. crossref(new window)

23.
Kaufman, Y.J., D. Tanré, L.A. Remer, E.F. Vermote, A. Chu, and B.N. Holben, 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067.

24.
Kim, H.-O., J.-M. Yeom, and Y.-S. Kim, 2011. The multi-temporal characteristics of spectral vegetation indices for agricultural land use on RapidEye satellite imagery, Aerospace Engineering and Technology, 10(1): 149-155 (in Korean with English abstract).

25.
Liang, X., D.P. Lettenmaier, E.F. Wood, and S.J. Burges, 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models, JOURNAL OF GEOPHYSICAL RESEARCH-ALL SERIES-, 99(D7): 14415-14428. crossref(new window)

26.
Liu, H. qing and A. Huete, 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise, IEEE Transactions on Geoscience and Remote Sensing, 33(2): 457-465. crossref(new window)

27.
KMA, 2009. Development of Meteorological Data Processing System for Communication, Ocean and Meteorological Satellite. KMA (in Korean with English abstract).

28.
Neteler, M., 2010. Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data, Remote Sensing, 2(1): 333-351. crossref(new window)

29.
Owe, M., R. de Jeu, and T. Holmes, 2008. Multisensor historical climatology of satellite-derived global land surface moisture, Journal of Geophysical Research: Earth Surface, 113(F1): F01002.

30.
Park, J., J.H. Kim, and S.J. Hong, 2012. Detection of Yellow Sand Dust over Northeast Asia using Background Brightness Temperature Difference of Infrared Channels from MODIS, Atmosphere, 22(2): 137-147 (in Korean with English abstract). crossref(new window)

31.
Park, K.-H. and S.-K. Jung, 1999. Analysis on Urban Heat Island Effects for the Metropolitan Green Space Planning, Journal of the Korean Association of Geographic Information Studies, 2(3): 35-45 (in Korean with English abstract).

32.
Sellers, P.J., C.J. Tucker, G.J. Collatz, S.O. Los, C.O. Justice, D.A. Dazlich, and D.A. Randall, 1996. A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data, Journal of Climate, 9(4): 706-737. crossref(new window)

33.
Shin, D., H. Park, and J.H. Kim, 2013. Analysis of the Fog Detection Algorithm of DCD Method with SST and CALIPSO Data, Atmosphere, 23(4): 471-483 (in Korean with English abstract). crossref(new window)

34.
Streutker, D.R., 2003. Satellite-measured growth of the urban heat island of Houston, Texas, Remote Sensing of Environment, 85(3): 282-289. crossref(new window)

35.
Su, Z., 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrology and Earth System Sciences Discussions, 6(1): 85-100. crossref(new window)

36.
Tachikawa, T., M. Kaku, A. Iwasaki, D.B. Gesch, M.J. Oimoen, Z. Zhang, and C. Carabajal, 2011. ASTER global digital elevation model version 2-summary of validation results. NASA.

37.
Tran, H., D. Uchihama, S. Ochi, and Y. Yasuoka, 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities, International Journal of Applied Earth Observation and Geoinformation, 8(1): 34-48. crossref(new window)

38.
Wan, Z., P. Wang, and X. Li, 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA, International Journal of Remote Sensing, 25(1): 61-72. crossref(new window)

39.
Weng, Q., 2003. Fractal analysis of satellite-detected urban heat island effect, Photogrammetric engineering & remote sensing, 69(5): 555-566. crossref(new window)

40.
Wilson, M.F. and A. Henderson-Sellers, 1985. A global archive of land cover and soils data for use in general circulation climate models, Journal of CLIMATOL, 5(2): 119-143. crossref(new window)

41.
Yuan, F. and M.E. Bauer, 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sensing of Environment, 106(3): 375-386. crossref(new window)

42.
Yun, H.C., M.G. Kim, and K.Y. Jung, 2013. Analysis of Temperature Change By Forest Growth for Mitigation of the Urban Heat Island, Journal of Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 31(2): 143-150 (in Korean with English abstract). crossref(new window)

43.
Zhou, L., R.E. Dickinson, Y. Tian, J. Fang, Q. Li, R.K. Kaufmann, and R.B. Myneni, 2004. Evidence for a significant urbanization effect on climate in China, Proc. of the National Academy of Sciences of the United States of America, 101(26): 9540-9544.