Yer yüzey sıcaklığının Becker ve LI-1990 algoritmasına bağlı hesaplanması

Yer yüzey sıcaklığı; dünyadaki birçok fiziksel, kimyasal, biyolojik, enerji ve su döngüsü, hava tahmini, global okyanus dolaşımı ve iklimsel değişimlerin belirlenmesi için önemli bir faktördür. Ayrıca yer yüzey sıcaklık bilgisi dünya enerji kaynaklarının yönetiminde ve çevresel çalışmalar için gereklidir. Bu amaçla çalışmada, NOAA–12,14,15/AVHRR uydu verileri kullanarak yer yüzey sıcaklığı haritaları oluşturulmuştur. Haritalarda Adana, Ankara, Antalya, Artvin, İstanbul, İzmir, Kayseri, Konya, Malatya, Samsun,Sivas, Şanlıurfa, Van illeri kontrol noktaları olarak seçilmiştir. Haritalardan elde edilen değerler, yer değerleri ile karşılaştırılmış ve aylık ortalama bazda korelasyon katsayısı (r) ve hataların karelerinin ortalamasının karekökü (RMSE) değerleri sırasıyla 0,989 ve 1,493 °K bulunmuştur. İllere göre korelasyon katsayısı ve RMSE değerleri sırasıyla 0,959–0,990 ve 1,589–3,332 °K arasında değişmiştir. Karşılaştırma neticesinde, NOAA-AVHRR uydu verileri kullanılarak yer yüzey sıcaklığının çok yüksek oranda doğrulukla hesaplanabileceği görülmüştür.

Calculation land surface temperature depending on Becker and LI-1990 algorithm

Land surface temperature is an important factor for determination of most physical, biological, energy and water processes and cycles, weather prediction, global ocean circulation and climatic variability in the earth. Further, knowledge of land surface temperature is necessary for management of earth energy resources and environmental studies. For these aims, land surface temperature maps were constituted by using NOAA–12,14,15/AVHRR satellite data. Cities of Adana, Ankara, and Antalya, Artvin, İstanbul, İzmir, Kayseri, Konya, Malatya, Samsun, Sivas, Şanlıurfa and Van were chosen as control points on the maps. The values which were obtained from the maps produced were compared with these ground-truth values. On monthly avereges of overall comparisons, the correlation coefficient (r) and root mean squared error (RMSE) value were found to be 0,989 and 1,493 °K respectively. When the separate cities were considered, correlation coefficient and RMSE values were found to change within the intervals 0,959–0,990 and 1,589–3,332 °K respectively. These show that land surface temperatures can be determined with a high accuracy by using the data from NOAA-AVHRR satellites.

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  • Altuntaş, C. ve Çorumluoğlu, Ö., Uzaktan Algılama Görüntülerinde Digital Görüntü İşleme ve Rsimage Yazılımı. Selçuk Üniversitesi Jeodezi ve Fotogrametri Mühendisliği Öğretiminde 30. Yıl Sempozyumu, Konya, 434-442, 2002.
  • Becker, F., The Impact of Spectral Emissivity on the Measurement of Land Surface Temperature, International Journal of Remote Sensing, 8, 1509 – 1522, 1987.
  • Becker, F. and Li, Z. L., Towards a Local Split Window Method over Land Surface, International Journal of Remote Sensing, 11,369 – 393, 1990.
  • Becker, F. and Li, Z. L., Surface Temperature and Emissivity at Various Scales: Defination, Measurement and Related Problems, Remote Sensing Reviews, 12, 225- 253, 1995.
  • Caselles, V., Coll, C. and Valor, E., Land Surface Emissivity and Temperature Determination in the Whole Hapex- Sahel Area from AVHRR Data, International Journal of Remote Sensing 18, 1009- 1027, 1997.
  • Chrysoulakis, N. and Cartalis, C., Improving the Estimation of Land Surface Temperature for the Region of Greece: Adjustment of a Split Window Algorithm to Account for the Distribution of Precipitable Water. International Journal of Remote Sensing, 23, 871–880, 2002.
  • Coll, C. and Caselles, V., A Split-Window Algorithm for Land Surface Temperature from Advanced Very High-Resolution Radiometer Data: Validation and Algorithm Comparison, Journal of Geophysical Research, 102, 16697–16713, 1997.
  • Coll, C., Caselles, V., Sobrino, J. A. and Valor, E., On the Atmospheric Dependence of the Split-Window Equation for Land Surface Temperature, International Journal of Remote Sensing, 15, 105-122, 1994.
  • Faysash, D. A. and Smith, E. A.,Simultaneous Land Surface Temperature-Emissivity Retrieved in the Infrared Split-Window, J. Atmos. Oceanic Technol., 16,1673-1689, 1999.
  • Gates, D., M., Biophysical Ecology, Springer-Verlag, New York, 1980.
  • Goodrum, G., Kidwell, K. B. and Winston,W. (14 Eylül 2005). NOAA KLM User's Guide Section 7.1. http://www2.ncdc.noaa.gov/docs/klm/html/c7/sec7-1.htm (Erişim tarihi:11 Aralık 2008).
  • Holben, B. N., Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data, International Journal of Remote Sensing, 7, 1417-1434, 1986.
  • Kant, Y. and Badarinath, K. V. S., Studies on Land Surface Temperature over Heterogeneous Areas Using AVHRR Data, Int. J. Remote Sensing, 21, 1749–1756, 2000.
  • Kendall, M. G. and Stuart, A., The Advanced Theory of Statistics, (Griffin Pres), London, 1963.
  • Kneizys, F. X., Shettle, E. P., Gallery, W. O., Chetwynd Jr, J. H. and Abreu, L. W., Atmospheric Trasmittance / Radiance: Computer Code LOWTRAN 6, Technical Report AFGL - Tr -83-0187, Optical Physics Division , U.S. Air Force Geophysics Laboratory, Hanscom Air Force Base, Massachusetts,U.S.A., 1983.
  • Laurent, H., Jobard, I. and Toma, A., Validation of Satellite and Ground-Based Estimates of Precipitation over the Sahel, Atmospheric Research, 47-48,651-670, 1998.
  • Liang, S., An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery, IEEE Transactions on Geoscience and Remote Sensing, 39,264-274, 2001.
  • Ma, X. L., Wan, Z., Moeller ,C. C., Menzel ,W. P. and Gumley, L. E., Simultaneous Retrieval of Atmospheric Profiles, Land Surface Temperature and Surface Emissivity from Moderate Resolution Imaging Spectroradiometer Thermal Infrared Data: Extension of a Two-Step Physical Algorithm, Appl.Opt., 41, 909-924, 2002.
  • Mather, P. M., Computer Processing of Remotely-Sensed Images: An Introduction, (3rd Edition), University of Nottingham, England , 2004.
  • Myneni, R. B., Hall, F. G., Sellers, P. J. and Marshak A. L., The Interpretation of Spectral Vegetation Indexes, IEEE Transactions on Geoscience and Remote Sensing, 33,481-486, 1995.
  • Prata, A. J. and Cechet, R. P., An Assessment of the Accurancy of Land Surface Temperature Determination from the GMS-5 VISSR, Remote Sensing of Environment, 67, 1–14, 1999.
  • Prata, A. J., Land Surface Temperature Derived from the Advanced Very High Resolution Radiometer and the Along-Track Scanning Radiometer.I:Theory, Journal of Geophysical Research, 98,16689-16702, 1993.
  • Prata, A. J., Land Surface Temperature Derived from the Advanced Very High Resolution Radiometer and the Along-Track Scanning Radiometer.II.Experimental Results and Validation of AVHRR Algorithms, Journal of Geophysical Research, 99 (D6), 13025-13058, 1994.
  • Price, J. C., Land Surface Temperature Measurements from the Split Window Channels of the NOAA 7 Advanced Very High Resolution Radiometer, Journal of Geophysical Research, 89,7231-7237, 1984.
  • Rao,C. R. N., Pre-Launch Calibration of Channels 1 and 2 of the Advanced Very High Resolution Radiometer, NOAA Technical Report NESDIS 36, Department of Commerce, Washington D.C., 1987.
  • Rao, C. R. N., Chen, J., Inter-Satellite Calibration Linkages For the Visible and Near-Infrared Channels of The Advanced Very High Resolution Radiometer on NOAA-7, -9 And -11 Spacecraft, International Journal of Remote Sensing, 16, 1931-1942, 1995.
  • Sellers, P. J., Canopy Reflectance, Photosynthesis and Transpiration, International Journal of Remote Sensing 6, 1335-1372, 1985.
  • Sobrino, J. A., Li, Z. L., Stoll, Z. P. and Becker, F., Improvements in the Split-Window Technique for Land Surface Temperature Determination, IEEE Transactions on Geoscience and Remote Sensing, 32, 243 – 253,1994.
  • Sobrino, J. A., Li, Z. L., Stoll, Z. P. and Becker, F., Multi-channel and Multi-Angle Algorithms for Estimating Sea and Land Surface Temperature with ATSR Data, International Journal of Remote Sensing, 17, 2089-2114, 1996.
  • Srivastava, S. K., Jayaraman,V., Rao, P. P. N., Manikiam, B. and Chandrasekhar, M. G., Interlinkages of NOAA/AVHRR Derived Integrated NDVI to Seasonal Precipitation and Transpiration in Dryland Tropics, International Journal of Remote Sensing, 18,2931-2952, 1997.
  • Sun, D. and Pinker, R. T., Estimation Land Surface Temperature from a Geostationary Operational Environment Satellite (GOES-8), J. Geophys. Res, 108, 4326, doi:10.1029/2002JD002442, 2003.
  • Sun, D., Pinker, R. T. and Basara, J. B., Land Surface Temperature Estimation from the Next Generation of Geostationary Operational Environmental Satellite: GOES M-Q, Journal of Appied meteorology, 43,363-372, 2004.
  • Şahin, M., Yer Yüzey Sıcaklığı, Atmosferik Nem Açıklığı ve Yağış Miktarının Uydu Verileri Kullanılarak Belirlenmesi, Çukurova Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, Adana, 2008.
  • Valor, E. and Caselles, V., Mapping land surface emissivity from NDVI:Application to Europen, African, and South American Areas, Remote Sensing of Environment, 57, 167-184,1996.
  • Van de Griend, A. A. and Owe, M., On the Relationship Between Thermal Emissivity and The Normalized Difference Vegetation Index for National Surfaces, International Journal of Remote Sensing, 14, 1119-1131, 1993.
  • Vidal, R. C. and Blad, B. L., Atmospheric and Emissivity Correction of Land Surface Temperature Measured from Satellite Using Ground Measurements or Satellite Data, International Journal of Remote Sensing,12,2449-2460, 1991.
  • Wan, Z. and Dozier, J., A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space, IEEE Transactions on Geoscience and Remote Sensing, 34,892-905, 1996.
Isı Bilimi ve Tekniği Dergisi-Cover
  • ISSN: 1300-3615
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 1977
  • Yayıncı: TÜRK ISI BİLİMİ VE TEKNİĞİ DERNEĞİ