Comparison between land surface temperature estimation in single and multi-channel method using LandSat images 8
Subject Areas : Geospatial systems developmentParvaneh Asgarzadeh 1 , Ali Darvishi Boloorani 2 , Hossain Ali Bahrami 3 , Saeid Hamzeh 4
1 - MSc. Graduated of Remote Sensing and GIS, University of Tehran
2 - Assis. Prof. College of Geography, University of Tehran
3 - Prof. College of Agiculture, Tarbiat Modares University
4 - Assis. Prof. College of Geography, University of Tehran
Keywords: Thermal remote sensing, Land surface temperature, LANDSAT 8, Thermal bands, Emissivity,
Abstract :
Land surface temperature (LST) is a key parameter in environmental studies particularly for drought monitoring. Due to the ground limitations to measure the LST on a large scale, thermal remote sensing is a unique method for estimating LST. The aim of this article is comparing between LST estimation in single and multi-channel method using Landsat 8 thermal and reflective bands. Necessary ground data from meteorological stations Farabi (Khuzestan) and Karaj (Alborz) were taken to coincide with the dates and times of Landsat 8 overpasses. In this article Land surface emissivity and atmospheric water vapor content are major inputs for single and multi-channel LST estimation. After correction, processing and calculation of interest, LST were estimated. For result evaluation, statistical indices such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2) were used. Results show the high value of R2 in all LST estimation method in comparison with ground measurement. In single channel using band 10 highest accuracy with MAE about 1.04 and 0.98 degrees in Karaj and Farabi station was seen respectively. The lowest and highest value of RMSE is in the single channel method (band 10) and multi-channel method (band 10 and 11) respectively. Study area conditions in terms of temperature; land cover and water vapor content affect the results and appropriate thermal band selection. Take-in consideration, especially using multi-band LST estimation method is suggested.
1. ابراهیمی هروی، ب.، ک. رنگزن، ح. ر. ریاحی بختیاری و ا. تقیزاده. 1394. تعیین درجه حرارت سطح اراضی شهری با استفاده از تصاویر ماهوارۀ لندست (مطالعة موردی: کرج). سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6(4): 19-32.
2. اسلمی، ف.، ا. قربانی، ب. سبحانی و م. پناهنده. 1394. مقایسة روشهای شبکه عصبی مصنوعی، ماشین بردار پشتیبان و شیءگرا در استخراج کاربری و پوشش اراضی از تصاویر لندست 8. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6(3): 1-14.
3. میرزاییزاده، و.، م. نیکنژاد و ج. اولادی قادیکلایی. 1394. ارزیابی الگوریتمهای طبقهبندی نظارت شده غیرپارامتریک در تهیة نقشه پوشش زمین با استفاده از تصاویر لندست 8. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6(3): 29-44.
4. Allen RG, Tasumi M, Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of Irrigation and Drainage Engineering, 133(4): 380-394.
5. Anderson MC, Allen RG, Morse A, Kustas WP. 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, 122: 50-65.
6. Bernstein LS, Jin X, Gregor B, Adler-Golden SM. 2012. Quick atmospheric correction code: algorithm description and recent upgrades. Optical Engineering, 51(11): 1-12.
7. Cristóbal J, Jiménez‐Muñoz J, Sobrino J, Ninyerola M, Pons X. 2009. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research: Atmospheres, 114(8): 1-16.
8. Cunha A, Alvalá R, Nobre C, Carvalho M. 2015. Monitoring vegetative drought dynamics in the Brazilian Semiarid Region. Agricultural and Forest Meteorology, 214: 494-505.
9. Dube T, Mutanga O. 2015. Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS Journal of Photogrammetry and Remote sensing, 108: 12-32.
10. Jeong S, Howat IM. 2015. Performance of Landsat 8 Operational Land Imager for mapping ice sheet velocity. Remote Sensing of Environment, 170: 90-101.
11. Jiménez-Muñoz JC, Cristóbal J, Sobrino JA, Sòria G, Ninyerola M, Pons X. 2009. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 339-349.
12. Jiménez-Muñoz JC, Sobrino JA, Gillespie A, Sabol D, Gustafson WT. 2006. Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 103(4): 474-487.
13. Jiménez-Muñoz JC, Sobrino JA, Skoković 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.
14. Ke Y, Im J, Lee J, Gong H, Ryu Y. 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sensing of Environment, 164: 298-313.
15. 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.
16. Montanaro M, Gerace A, Lunsford A, Reuter D. 2014. Stray light artifacts in imagery from the Landsat 8 Thermal Infrared Sensor. Remote Sensing, 6(11): 10435-10456.
17. Peña M, Brenning A. 2015. Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile. Remote Sensing of Environment, 171: 234-244.
18. 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. International Journal of Remote Sensing, 22(18): 3719-3746.
19. Quintano C, Fernández-Manso A, Calvo L, Marcos E, Valbuena L. 2015. Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. International Journal of Applied Earth Observation and Geoinformation, 36:1-12.
20. Roy DP, Wulder M, Loveland T, Woodcock C, Allen R, Anderson M, Helder D, Irons J, Johnson D, Kennedy R. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145: 154-172.
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(4): 5768-5780.
22. Senay GB, Bohms S, Singh RK, Gowda PH, Velpuri NM, Alemu H, Verdin JP. 2013. Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. JAWRA Journal of the American Water Resources Association, 49(3): 577-591.
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. Wang L, Koike T, Yang K, Yeh PJ-F. 2009. Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin. Journal of Hydrology, 377(1): 21-34.
25. Wu M, Li H, Huang W, Niu Z, Wang C. 2015. Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring. Environmental Science: Processes & Impacts, 17(8): 1396-1404.
26. Yang J, Wong MS, Menenti M, Nichol J. 2015. Study of the geometry effect on land surface temperature retrieval in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 109: 77-87.
27. Yıldız BY, Şahin M, Şenkal O, Peştimalci V, Tepecik K. 2014. Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey. Advances in Space Research, 54(8): 1544-1551.
28. 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(10): 9829-9852.