Journals / Isı Bilimi ve Tekniği Dergisi / 2010 / Cilt: 30 - Sayı: 1

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

Yer yüzey sıcaklığının Becker ve LI-1990 algoritmasına bağlı hesaplanması

Pages
35–43
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:For these aims, land surface temperature maps were constituted by using NOAA–12,14,15/AVHRR satellite data.
💡
Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

Özet

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.

🔖

Cite This Article

KANDIRMAZ, H. M., ŞAHİN, M. (2022). Calculation land surface temperature depending on Becker and LI-1990 algorithm. Isı Bilimi ve Tekniği Dergisi, Cilt: 30 - Sayı: 1, 35–43. https://ajindex.com/articles/542600

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. 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.
  2. Becker, F., The Impact of Spectral Emissivity on the Measurement of Land Surface Temperature, International Journal of Remote Sensing, 8, 1509 – 1522, 1987.
  3. Becker, F. and Li, Z. L., Towards a Local Split Window Method over Land Surface, International Journal of Remote Sensing, 11,369 – 393, 1990.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Gates, D., M., Biophysical Ecology, Springer-Verlag, New York, 1980.
  11. 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).
  12. Holben, B. N., Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data, International Journal of Remote Sensing, 7, 1417-1434, 1986.
  13. 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.
  14. Kendall, M. G. and Stuart, A., The Advanced Theory of Statistics, (Griffin Pres), London, 1963.
  15. Kneizys, F. X., Shettle, E. P., Gallery, W. O., Chetwynd Jr, J. H. and Abreu, L. W.,