Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

Kun TAN, Zhihong LIAO, Peijun DU, Lixin WU

PDF(5645 KB)
PDF(5645 KB)
Front. Earth Sci. ›› 2017, Vol. 11 ›› Issue (1) : 20-34. DOI: 10.1007/s11707-016-0570-7
RESEARCH ARTICLE
RESEARCH ARTICLE

Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

Author information +
History +

Abstract

A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

Keywords

Land surface temperature (LST) / split-window algorithm / emissivity / Landsat 8

Cite this article

Download citation ▾
Kun TAN, Zhihong LIAO, Peijun DU, Lixin WU. Land surface temperature retrieval from Landsat 8 data and validation with geosensor network. Front. Earth Sci., 2017, 11(1): 20‒34 https://doi.org/10.1007/s11707-016-0570-7

References

[1]
Becker F, Li Z L (1995). Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sens Rev, 12(3‒4): 225–253
CrossRef Google scholar
[2]
Berk A, Anderson G, Acharya P, Chetwynd J, Bernstein L, Shettle E, Matthew M, Adler-Golden S (1999). MODTRAN4 user’s manual. Air Force Research Laboratory, Space Vehicles Directorate, 1–5
[3]
Berk A, Bernstein L S, Robertson D C (1987). MODTRAN: a moderate resolution model for LOWTRAN. In: DTIC Document
[4]
Du C, Ren H, Qin Q, Meng J, Li J ( 2014). Split-Window algorithm for estimating land surface temperature from Landsat 8 TIRS data. In: Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 3578–3581
[5]
Harris A, Mason I (1992). An extension to the split-window technique giving improved atmospheric correction and total water vapour. Int J Remote Sens, 13(5): 881–892
CrossRef Google scholar
[6]
Jiménez-Muñoz J C ( 2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. J Geophys Res, 108,
CrossRef Google scholar
[7]
Jiménez-Muñoz J C, Cristóbal J, Sobrino J A, Sòria G, Ninyerola M, Pons X ( 2009). Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. Geoscience and Remote Sensing. IEEE Transactions on, 47: 339–349
[8]
Jimenez-Munoz J C, Sobrino J A, Skokovic D, Mattar C, Cristobal J ( 2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett, 11(10): 1840–1843
CrossRef Google scholar
[9]
Kneizys F, Shettle E, Gallery W, Chetwynd J Jr, Abreu L (1983). Atmospheric transmittance/radiance: computer code LOWTRAN 6. Supplement: Program listings. In: DTIC Document
[10]
Kneizys F X, Shettle E, Abreu L, Chetwynd J, Anderson G (1988). Users guide to LOWTRAN 7. In: DTIC Document
[11]
Krishna A P, Sharma A ( 2013). Snow cover and land surface temperature assessment of Gangotri basin in the Indian Himalayan Region (IHR) using MODIS satellite data for climate change inferences. In, SPIE Remote Sensing (pp. 889314-889314-889319): International Society for Optics and Photonics
[12]
Lu D, Weng Q (2006). Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sens Environ, 104: 157–167
[13]
Mao K B, Qin Z H, Shi J C, Gong P ( 2005a). The research of Split-Window Algorithm on the MODIS. Geomatics and Information Science of Wuhan University, 30(8): 703–707
[14]
Mao K, Qin Z, Shi J, Gong P ( 2005b). A practical split-window algorithm for retrieving land-surface temperature from MODIS data. Int J Remote Sens, 26(15): 3181–3204
CrossRef Google scholar
[15]
Patel N, Parida B, Venus V, Saha S, Dadhwal V ( 2012). Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data. Environ Monit Assess, 184(12): 7153–7163
CrossRef Google scholar
[16]
Price J C (1983). Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect. Remote Sens Environ, 13(4): 353–361
CrossRef Google scholar
[17]
Price J C (1984). Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research: Atmospheres (1984–2012), 89: 7231–7237
[18]
Qin Z, Dall’Olmo G, Karnieli A, Berliner P ( 2001a). Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA‐advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres (1984–2012), 106: 22655–22670
[19]
Qin Z, Karnieli A, Berliner P ( 2001b). 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(18): 3719–3746
CrossRef Google scholar
[20]
Qin Z, Li W, Gao M, Zhang H( 2006). Estimation of land surface emissivity for Landsat TM6 and its application to Lingxian Region in north China. Proc SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 636618
CrossRef Google scholar
[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 (Basel Switzerland), 14: 5768–5780
[22]
Sasamori N (1999). Calculation of emissivity by measuring reflectance and transmittance. Report of Tokyo Metropolitan Industrial Technology Research Institute,2:45–48
[23]
Schädlich S, Göttsche F, Olesen F S ( 2001). Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sens Environ, 75(1): 39–46
CrossRef Google scholar
[24]
Sibo D, Guangjian Y, Yonggang Q ( 2008). Two single-channel algorithms for retrieving land surface temperature from the simulated HJ-1B data. Prog Nat Sci, 18: 1001–1008
[25]
Sobrino J A, Jiménez-Muñoz J C, Paolini L ( 2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ, 90(4): 434–440
CrossRef Google scholar
[26]
Sobrino J A, Li Z L, Stoll M P (1993). Impact of the atmospheric transmittance and total water vapor content in the algorithms for estimating satellite sea surface temperatures. Geoscience and Remote Sensing. IEEE Transactions on, 31: 946–952
[27]
Susskind J, Rosenfield J, Reuter D, Chahine M (1984). Remote sensing of weather and climate parameters from HIRS2/MSU on TIROS-N. Journal of Geophysical Research: Atmospheres (1984–2012), 89: 4677–4697
[28]
Valor E, Caselles V (1996). Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sens Environ, 57(3): 167–184
CrossRef Google scholar
[29]
Van de Griend A, Owe M (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int J Remote Sens, 14(6): 1119–1131
CrossRef Google scholar
[30]
Wan Z ( 2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens Environ, 140: 36–45
CrossRef Google scholar
[31]
Wan Z, Dozier J (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. Geoscience and Remote Sensing. IEEE Transactions on, 34: 892–905
[32]
Weng Q, Lu D ( 2008). A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int J Appl Earth Obs Geoinf, 10(1): 68–83
CrossRef Google scholar
[33]
Wukelic G E, Gibbons D E, Martucci L M, Foote H P (1989). Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sens Environ, 28: 339–347
CrossRef Google scholar
[34]
Yang L, Cao Y, Zhu X, Zeng S, Yang G, He J, Yang X ( 2014). Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China. Journal of Arid Land, 6(6): 704–716
CrossRef Google scholar
[35]
Yong Z, Yu T, Xingfa G, Yuxiang Z, Fuliang Z, Shanshan Y, Wenjun Z, Xiaowen L ( 2006). Land surface temperature retrieval from CBERS-02 IRM SS thermal infrared data and its applications in quantitative analysis of urban heat island effect. Journal of Remote Sensing, 10: 789–797
[36]
Zhou J, Li J, Zhao X, Zhan W F, Guo J X ( 2011). A modified single-channel algorithm for land surface temperature retrieval from HJ-1 B satellite data. Journal of Infrared and Millimeter Waves, 30(1): 61–67
CrossRef Google scholar

Acknowledgment

The authors would like to thank Professors Dengsheng Lu and Wengfeng Zhan for their highly constructive remarks. This research was supported in part by the National Natural Science Foundation of China (Grant No. 41471356), the Fundamental Research Funds for the Central Universities (2014QNA33), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. The authors would also like to thank the Jiangsu Innovation Team at CUMT and MASTRO.

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(5645 KB)

Accesses

Citations

Detail

Sections
Recommended

/