Estimation of Ardabil land surface temperature using Landsat images and accuracy assessment of land surface temperature estimation methods with ground truth data
Subject Areas : Geospatial systems developmentHossein Fekrat 1 , Sayyad Asghari Saraskanrood 2 , Seyed Kazem Alavipanah 3
1 - MSc. Student of Remote Sensing and GlS, Faculty of Humanities, University of Mohaghegh Ardabili, Iran
2 - Associate Professor, Department of Natural Geography, Faculty of Humanities, University of Mohaghegh Ardabili, Iran
3 - Professor, Department of Remote Sensing and GlS, Faculty of Geography, University of Tehran, Iran
Keywords: Emissivity, Digital thermometer, Ardabil, Land surface temperature (LST),
Abstract :
Background and ObjectiveOver the past two decades, the intense need for land surface temperature information for environmental studies and management and planning activities has made estimating the land surface temperature one of the most important scientific topics. On the other hand, different methods have been proposed to estimate the land surface temperature, each of which has resulted in different results for different regions. In this study, the algorithms that have had acceptable results in different studies have been selected and evaluated. In the field of thermal studies, what is considered as a major defect in monitoring the land surface temperature is the lack of sufficient meteorological stations to know the temperature values in places without stations and information limitations in preparing temperature data, especially for large areas. The study area is also facing this shortage, and this limitation further highlights the importance of the topic selected for this study to estimate the surface temperature using remote sensing technology. Verification and validation of results obtained from estimating the land surface temperature are other basic and discussed topics in thermal studies. The purpose of this study is an estimation of temperature in Ardabil city and evaluate the accuracy of the four single-channel algorithms, the improved mono-window, the Planck's inversion function method and the radiative transfer equation (RTE) method, to compare the accuracy of the two Landsat 5 and Landsat 8 satellites in estimating the land surface temperature. Materials and Methods Three types of data have been used in this study; Landsat 5 and 8 satellite images, data of two meteorological stations and ground data harvested with a digital thermometer. The images used are from the two satellites Landsat 5 and Landsat 8 with a time interval of 19 years. The meteorological data used were obtained from two synoptic stations in the study area. In addition to land surface temperature, relative humidity, minimum temperature and maximum temperature data of 24 hours were also obtained on two dates. Also, two points of the study area were selected and land surface temperature in the position of these two stations simultaneously with the satellite Recorded from two digital thermometers. MODTRAN web version calculator software version 6 has been used to model the radiation and the amount of atmospheric transmission. Emissivity with two methods of LSE methods based on NDVI and LSE NDVI Thresholds Method and land surface temperature with four algorithms: single-channel algorithms, An Improved mono-window, inversion of Planck’s function and radiative transfer equation using band 6 Landsat 5 and band 10 Landsat 8 bands. It was coded in MATLAB software for 2000 and 2019. Finally, the accuracy of the algorithms was evaluated using synoptic station surface temperature data and field sampling. Results and Discussion The collected data and results are analyzed and while presenting the output maps, the accuracy of the methods with terrestrial and meteorological data as well as the accuracy of Landsat 5 and Landsat 8 satellites in estimating the land surface temperature has been compared and evaluated. The results showed that for the three single-channel algorithms, the inversion of Planck’s function and RTE, the first method of emission and for the An Improved Mono-Window algorithm, the second method of emission had a higher accuracy. Land surface temperature data obtained from meteorological stations in 2000 differ by 12 minutes in terms of time and by 2019 differ by 4 minutes in terms of satellite transit time. The first meteorological station is located somewhat within the city limits and according to the results, it seems that the most important factor is the greater difference between the data of the first station and the estimated LST compared to the second station is the same factor because the heterogeneity of pixels and large changes in levels in urban areas interfere with a pixel value. And subsequently increases the likelihood of errors in estimating surface temperature within the urban anthropogenic range. For the ground station, two points with a homogeneous environment and outside the urban area with agricultural use (alfalfa) and barren use of the harvested product were selected and their surface temperature was measured at the same time as the satellite. The output results of land surface temperature estimation were compared and evaluated with two synoptic stations and two ground stations. In both histories, the single-channel algorithm showed the least difference with the temperature recording stations. Conclusion In this research, using Landsat 5 and Landsat 8 satellite images, four algorithms for estimating the land surface temperature of the earth, including single-channel algorithms, An Improved mono-window, inversion of Planck’s function and radiative transfer equation and land surface temperature maps of Ardabil city for two 2000 and 2019 were coded and extracted in MATLAB software environment. The band 6 Landsat 5 satellite was used for 2000 and the band 10 Landsat 8 satellite was used for 2019 due to less noise than the 11th band and the proximity of 9.66 (which is the highest radiation in this range). Comparison of land surface temperature maps obtained by the algorithms with synoptic and ground stations showed that in both 2000 and 2019, the single-channel algorithm was more accurate than the other methods. Comparison of the results of the single-channel method with the stations shows a difference of +2.5 and 2- with stations 1 and 2 for the year 2000 and a temperature difference of +3.3, +0.9, 1- and -0.9. Shows stations 1, 2, 3 and 4 for 2019, respectively. It seems that the direct use of atmospheric transmittance coefficients in the single-channel method process has been effective in the high accuracy of this method. In terms of accuracy, after the single-channel algorithm, the An Improved Mono-Window method, the RTE algorithm, and finally the Planck function inverse correlation algorithm were placed, respectively. The results of comparing the output of all four algorithms with the data of stations 1, 2, 3 and 4, show that the ground stations harvested with a digital thermometer are more accurate than the data of meteorological stations. One of the reasons for this is the location of meteorological stations (especially, Station_1) in the urban area due to the heterogeneity of the urban environment and the possibility of pixel interference and temperature interference of land uses, while ground stations from the out-of-town area. And was selected from an environment with homogeneous pixels (barren and agricultural). Also, the results of all four algorithms extracted from the Landsat 8 image show more accuracy compared to the results of the four algorithms obtained from the Landsat 5 image, and due to the improved spatial resolution of the TIRS sensor compared to the TM, the TIRS sensor output is more accurate, It was predictable.
Asgarzadeh P, Darvishi Boloorani A, Bahrami H, Hamzeh S. 2016. Comparison between land surface temperature estimation in single and multi-channel method using LandSat images 8. Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 7(3): 18-29. (In Persian).
Asghari SS, Emami H. 2018. Monitoring the land surface temperature and examining the relationship between land use and land surface temperature using from OLI and ETM+ sensor images, (Case study: Ardabil city ). Journal of Geographical Sciences, 19(53): 195-215. (In Persian). doi:https://doi.org/10.29252/jgs.19.53.195.
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Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A, Ratkowski AJ, Felde G, Hoke ML. 2005. Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi-and hyperspectral imagery. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. International Society for Optics and Photonics, pp 668-678. https://doi.org/610.1117/1112.603359.
Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Cristóbal J, Jiménez-Muñoz JC, Prakash A, Mattar C, Skoković D, Sobrino JA. 2018. An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3): 431. doi:https://doi.org/10.3390/rs10030431.
Danodia A, Nikam R, Kumar S, Patel N. 2017. Land surface temperature retrieval by radiative transfer equation and single channel algorithms using landsat-8 satellite data. Indian Institute of Remote Sensing-ISRO: 1-7.
Feizizadeh B, Didehban K, Gholamnia K. 2016. Extraction of Land Surface Temperature (LST) based on landsat satellite images and split window algorithm Study area: Mahabad Catchment. Scientific-Research Quarterly of Geographical Data (SEPEHR), 25(98): 171-181. (In Persian).
García-Santos V, Cuxart J, Martínez-Villagrasa D, Jiménez MA, Simó G. 2018. Comparison of three methods for estimating land surface temperature from landsat 8-tirs sensor data. Remote Sensing, 10(9): 1450. doi:https://doi.org/10.3390/rs10091450.
Isaya Ndossi M, Avdan U. 2016. Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: a PyQGIS plugin. Remote sensing, 8(5): 413. doi:https://doi.org/10.3390/rs8050413.
Jiménez‐Muñoz JC, Sobrino JA. 2003. A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres, 108(D22). doi:https://doi.org/10.1029/2003JD003480.
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. doi:https://doi.org/10.1016/j.rse.2012.12.008.
Ndossi MI, Avdan U. 2016. Inversion of land surface temperature (LST) using Terra ASTER data: a comparison of three algorithms. Remote Sensing, 8(12): 993. doi:https://doi.org/10.3390/rs8120993.
Parastatidis D, Mitraka Z, Chrysoulakis N, Abrams M. 2017. Online global land surface temperature estimation from Landsat. Remote sensing, 9(12): 1208. doi:https://doi.org/10.3390/rs9121208.
Rubio E, Caselles V, Badenas C. 1997. Emissivity measurements of several soils and vegetation types in the 8–14, μm Wave band: Analysis of two field methods. Remote Sensing of Environment, 59(3): 490-521. doi:https://doi.org/10.1016/S0034-4257(96)00123-X.
Sahana M, Dutta S, Sajjad H. 2019. Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. International Journal of Urban Sciences, 23(2): 205-225. doi:https://doi.org/10.1080/12265934.2018.1488604.
Sajib MQU, Wang T. 2020. Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms. Sensors, 20(6): 1778. doi:https://doi.org/10.3390/s20061778.
Sinha S, Pandey PC, Sharma LK, Nathawat MS, Kumar P, Kanga S. 2014. Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote Sensing Applications in Environmental Research. Springer International Publishing, Cham, pp 57-68. https://doi.org/10.1007/1978-1003-1319-05906-05908_05904.
Sobrino J, Raissouni N. 2000. Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International journal of remote sensing, 21(2): 353-366. doi:https://doi.org/10.1080/014311600210876.
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. doi: https://doi.org/10.1109/TGRS.2007.904834.
Sobrino JA, Oltra-Carrió R, Jiménez-Muñoz JC, Julien Y, Sòria G, Franch B, Mattar C. 2012. Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX). International Journal of Applied Earth Observation and Geoinformation, 18: 141-147. doi:https://doi.org/10.1016/j.jag.2012.01.022.
Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T, Gupta M, Dai Q. 2014. Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product. Environmental Earth Sciences, 72(12): 5183-5196. doi:10.1007/s12665-014-3388-1.
USGS. 2016. Landsat 8 (L8) data users handbook. Landsat Science Official Website.
USGS. 2014. USGS earthexplorer. Retrieved from http://earthexplorer.usgs.gov/.
Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, De La Riva J. 2014. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5): 4345-4368. doi:https://doi.org/10.3390/rs6054345.
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(4): 4268-4289. doi:https://doi.org/10.3390/rs70404268.
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. doi:https://doi.org/10.3390/rs6109829.
Zakkula G. 1999. Elements of sampling theory and methods. Prentice Hall. 540 p.
Zhang J, Wang Y, Li Y. 2006. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, 32(10): 1796-1805. doi:https://doi.org/10.1016/j.cageo.2006.05.001.
Zhang Z, He G. 2013. Generation of Landsat surface temperature product for China, 2000–2010. International journal of remote sensing, 34(20): 7369-7375. doi:https://doi.org/10.1080/01431161.2013.820368.
_||_Asgarzadeh P, Darvishi Boloorani A, Bahrami H, Hamzeh S. 2016. Comparison between land surface temperature estimation in single and multi-channel method using LandSat images 8. Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 7(3): 18-29. (In Persian).
Asghari SS, Emami H. 2018. Monitoring the land surface temperature and examining the relationship between land use and land surface temperature using from OLI and ETM+ sensor images, (Case study: Ardabil city ). Journal of Geographical Sciences, 19(53): 195-215. (In Persian). doi:https://doi.org/10.29252/jgs.19.53.195.
Barsi JA, Barker JL, Schott JR. 2003. An Atmospheric Correction Parameter Calculator for a single thermal band earth-sensing instrument, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477). In., pp 21-25 July 2003, vol. 2005: 3014-3016 p.
Berk A, Conforti P, Kennett R, Perkins T, Hawes F, Van Den Bosch J. 2014. MODTRAN® 6: A major upgrade of the MODTRAN® radiative transfer code. In: 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, pp 1-4. https://doi.org/10.1109/WHISPERS.2014.8077573.
Bernstein LS, Adler-Golden SM, Sundberg RL, Levine RY, Perkins TC, Berk A, Ratkowski AJ, Felde G, Hoke ML. 2005. Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi-and hyperspectral imagery. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. International Society for Optics and Photonics, pp 668-678. https://doi.org/610.1117/1112.603359.
Carlson TN, Ripley DA. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252. doi:https://doi.org/10.1016/S0034-4257(97)00104-1.
Cristóbal J, Jiménez-Muñoz JC, Prakash A, Mattar C, Skoković D, Sobrino JA. 2018. An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3): 431. doi:https://doi.org/10.3390/rs10030431.
Danodia A, Nikam R, Kumar S, Patel N. 2017. Land surface temperature retrieval by radiative transfer equation and single channel algorithms using landsat-8 satellite data. Indian Institute of Remote Sensing-ISRO: 1-7.
Feizizadeh B, Didehban K, Gholamnia K. 2016. Extraction of Land Surface Temperature (LST) based on landsat satellite images and split window algorithm Study area: Mahabad Catchment. Scientific-Research Quarterly of Geographical Data (SEPEHR), 25(98): 171-181. (In Persian).
García-Santos V, Cuxart J, Martínez-Villagrasa D, Jiménez MA, Simó G. 2018. Comparison of three methods for estimating land surface temperature from landsat 8-tirs sensor data. Remote Sensing, 10(9): 1450. doi:https://doi.org/10.3390/rs10091450.
Isaya Ndossi M, Avdan U. 2016. Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: a PyQGIS plugin. Remote sensing, 8(5): 413. doi:https://doi.org/10.3390/rs8050413.
Jiménez‐Muñoz JC, Sobrino JA. 2003. A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres, 108(D22). doi:https://doi.org/10.1029/2003JD003480.
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. doi:https://doi.org/10.1016/j.rse.2012.12.008.
Ndossi MI, Avdan U. 2016. Inversion of land surface temperature (LST) using Terra ASTER data: a comparison of three algorithms. Remote Sensing, 8(12): 993. doi:https://doi.org/10.3390/rs8120993.
Parastatidis D, Mitraka Z, Chrysoulakis N, Abrams M. 2017. Online global land surface temperature estimation from Landsat. Remote sensing, 9(12): 1208. doi:https://doi.org/10.3390/rs9121208.
Rubio E, Caselles V, Badenas C. 1997. Emissivity measurements of several soils and vegetation types in the 8–14, μm Wave band: Analysis of two field methods. Remote Sensing of Environment, 59(3): 490-521. doi:https://doi.org/10.1016/S0034-4257(96)00123-X.
Sahana M, Dutta S, Sajjad H. 2019. Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. International Journal of Urban Sciences, 23(2): 205-225. doi:https://doi.org/10.1080/12265934.2018.1488604.
Sajib MQU, Wang T. 2020. Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms. Sensors, 20(6): 1778. doi:https://doi.org/10.3390/s20061778.
Sinha S, Pandey PC, Sharma LK, Nathawat MS, Kumar P, Kanga S. 2014. Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote Sensing Applications in Environmental Research. Springer International Publishing, Cham, pp 57-68. https://doi.org/10.1007/1978-1003-1319-05906-05908_05904.
Sobrino J, Raissouni N. 2000. Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International journal of remote sensing, 21(2): 353-366. doi:https://doi.org/10.1080/014311600210876.
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. doi: https://doi.org/10.1109/TGRS.2007.904834.
Sobrino JA, Oltra-Carrió R, Jiménez-Muñoz JC, Julien Y, Sòria G, Franch B, Mattar C. 2012. Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX). International Journal of Applied Earth Observation and Geoinformation, 18: 141-147. doi:https://doi.org/10.1016/j.jag.2012.01.022.
Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T, Gupta M, Dai Q. 2014. Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product. Environmental Earth Sciences, 72(12): 5183-5196. doi:10.1007/s12665-014-3388-1.
USGS. 2016. Landsat 8 (L8) data users handbook. Landsat Science Official Website.
USGS. 2014. USGS earthexplorer. Retrieved from http://earthexplorer.usgs.gov/.
Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, De La Riva J. 2014. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5): 4345-4368. doi:https://doi.org/10.3390/rs6054345.
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(4): 4268-4289. doi:https://doi.org/10.3390/rs70404268.
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. doi:https://doi.org/10.3390/rs6109829.
Zakkula G. 1999. Elements of sampling theory and methods. Prentice Hall. 540 p.
Zhang J, Wang Y, Li Y. 2006. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, 32(10): 1796-1805. doi:https://doi.org/10.1016/j.cageo.2006.05.001.
Zhang Z, He G. 2013. Generation of Landsat surface temperature product for China, 2000–2010. International journal of remote sensing, 34(20): 7369-7375. doi:https://doi.org/10.1080/01431161.2013.820368.