Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Developing an algorithm for automatic estimation of Land Surface Temperature(LST) using landsat 8 images - Case study: Tabriz City

Document Type : Research Paper

Authors
Javad Javdan 1
Mohammad Hossein Rezaei Moghaddam 2
Yousef Ebadi 1
1 MSc. Student of remote sensing and GIS, University of Tabriz
2 Depraments of geomorphology and GIS & RS, University of Tabriz
10.22131/sepehr.2020.38615
Abstract
Extended Abstract
Introduction
Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving land surface temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving land surface temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for LST estimation.
 Materials & Methods
In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data.Thermal band 10 was used to estimate brightness temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of LST from Landsat 8 TIRS data is performed based on the premise that brightness temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm.Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65,535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness temperature.Ground emissivity is calculatedusing land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional LST retrieval methods.
 Results & Discussion
The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of LST. After running the algorithm on the satellite images of the study area on July 18,2016, a lower land surface temperature was observed in green spaces with 1.2°C accuracy as compared to urban areas and wastelands. The lowest temperature in the study area was 20°C and the highest temperature was 53°C and mean temperature was 38.78°C.Results indicate that the algorithm candiscover natural urban heat islands accurately. Moreover, spatial distribution of LST in the region is quite well matched with the land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving LST from Landsat 8 data.
 Conclusion
Compared to common methods,the proposed algorithm estimates land surface temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute LST and apply it in other studies, and thus it is a significant tool.
Keywords
Mono-window algorithm
Land Surface Temperature
Thermal remote sensing
Landsat 8
MATLAB
  1. ابراهیمی هروی، ب.، ک. رنگزن، ح. ر. ریاحی بختیاری و ا. تقی زاده. 1394. تعیین درجه حرارت سطح اراضی شهری با استفاده از تصاویر ماهواره لندست (مطالعه موردی: کرج). مجله سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 6 (2): 19-32.
  2. جویباری مقدم، ی.، م. آخوندزاده و م. ر. سراجیان. 1394. ارائه یک الگوریتم پنجره مجزا نوین به منظور تخمین دمای سطح زمین از داده‌های ماهواره لندست – 8. مجله علوم و فنون نقشه‌برداری. 5 (1): 215-226.
  3. خضری، س. و ج. ملکی. 1390. بازیابی حرارت سطح و استخراج چشمه های آب گرم و سرد با استفاده از باند 6 سنجنده ETM+, TM (منطقه مورد مطالعه کردستان و اردبیل). مجله اطلاعات جغرافیایی (سپهر). 20 (79): 65-61.
  4. علوی پناه، س. ک. 1387 . سنجش از دور حرارتی. انتشارات دانشگاه تهران. چاپ دوم. 522 صفحه.
  5. فیضی زاده، ب.، خ. دیده بان و خ. غلام نیا. 1395. برآورد دمای سطح زمین با استفاده از تصاویر ماهواره لندست8 و الگوریتم پنجره مجزا (مطالعه موردی: حوضه آبریز مهاباد). مجله اطلاعات جغرافیایی (سپهر). 25 (98): 171-181.
  6. متکان، ع. ا.، ا. نوحه‌گر، ب. میرباقری و ن. ترک چین. 1393. تحلیل نقش کاربری اراضی در شکل گیری جزایر حرارتی با استفاده از داده‌های چندزمانه سنجنده ASTER (مطالعه موردی: شهر بندرعباس). مجله سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 5 (4): 1-14.
  7. موسوی کجاباد، ن. 1394. کاربرد تصاویر ماهواره‌ای حرارتی و اپتیکی در شناسایی گسل‌های فعال (مطالعه موردی: منطقه تبریز). پایان‌نامه کارشناسی ارشد سنجش از دور و GIS. دانشکده جغرافیا و برنامه‌ریزی، دانشگاه تبریز. 93 صفحه.
  8. ویسی، ش.، ع. ناصری، س.حمزه و پ. مرادی. 1395. برآورد دمای مزارع نیشکر با استفاده از الگوریتم پنجره مجزا و تصاویر سنجنده OLI ماهواره لندست 8. مجله سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 7 (1): 27-40.

9. Anding D, Kauth R. 1970. Estimation of Sea Surface Temperature from Space. Remote Sensing of Environment. 1 (4): 217-220.

10. Avdan U, Jovanovska G. 2016. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors. 2016: 1-8.

11. Barsi JA, Schott JR, Hook SJ, Raqueno NG, Markham BL, Radocinski RG. 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11): 11607–11626.

12. Bendib A, Dridi H, Kalla MI. 2016 . Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria. Geocarto International. 1-10.

13. Calson TN, Ripley DA. 1997. On the relation between NDVI, franctional vegetation cover, and leaf area index. Remote Sens. Environ. 62: 241–252.

14. Jiménez-Muñoz JC, Sobrino JA, Skokovic D, Mattar C, Cristóbal J. 2014. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters. 11 (10): 1840-1843.

15. Leuning R, Kelliher FM, Pury DGG, Schulze ED. 1995. Leaf nitrogen, photosynthesis, conductance and transpiration: scaling from leaves to canopies. Plant. Cell. Environ. 18 (10): 1183–1200.

16. Li Z-L, Tang B-H, Wu H, Ren H, Yan G,Wan Z, Trigo IF, Sobrino JA. 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment. 131: 14-37.

17. Peres LF, DaCamara CC. 2004. Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data. Remote Sensing of Environment, 91(3-4): 377-389.

18. Price JC. 1983. Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect. Remote Sensing of Environment. 13 (4): 353-361.

19. Qin Z, Karnieli A, Berliner P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens. 22: 3719–3746.

20. Quattrochi DA, Luvall JC. 2005. Thermal Remote Sensing in LandSurface Processes. CRC PRESS. 440 pages.

21. Rozenstein O, Qin Z, Derimian Y, Karnieli A. 2014. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors.14 (6): 5768–5780.

22. Sobrino JA, Coll C, Caselles V. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment. 38 (1): 19-34.

23. Sobrino JA, Jiménez-Muñoz JC, Sòria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martínez P. 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing. 46 (2): 316–327.

24. United States Geological Survey. Landsat 8 Operational Land Imager and Thermal Infrared Sensor calibration notices. Availanle online: https://landsat.usgs.gov/landsat-8-l8-operational-land-imager-oli-and-thermal-infrared-sensor-tirs (accessed on November 14, 2013).

25. Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S. 2015. An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sensing. 7: 4268–4289.

26. Yu X, Guo X, Wu Z. 2014. Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sensing. 6: 9829-9852.