Journal of East China Normal University(Natural Science) >

2016 , Vol. 2016 >Issue 4: 129 - 138

DOI: https://doi.org/10.3969/j.issn.1000-5641.2016.04.015

Retrieval of land surface temperature based on Landsat-8 thermal infrared data and heat island effect analysis over the Taihu Lake region

  • KAN Zeng-hui ,
  • LIU Chao-shun ,
  • LI Zhi-jun
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  • 1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China;
    2. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai 200241, China;
    3. Jingjiang Meteorological Bureau, Jingjiang Jiangsu 214500, China

Received date: 2015-07-08

  Online published: 2016-09-29

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Abstract

Using three different retrieval algorithms based on Landsat-8 thermal infrared data and meteorological data, land surface temperature (LST) of the Taihu Lake region is obtained. After comparing the values of three different algorithms with measured values, we find that the result of radiative transfer equation (RTE) is a good approximation to the measured values. When the real-time atmospheric profile is absent, general single-channel algorithm (SC) is more suitable for land surface temperature retrieval based on Landsat 8 thermal infrared data.The split window algorithm (SW) has the lowest accuracy. After analyzing the LST
characteristics of different land cover based on the result of RTE, we find that built-up areas has the highest LST while water body has the lowest LST. There is a negative correlation relationship between the normalized difference vegetation index (NDVI) and the LST values, which indicates that urban green space is a key way to improve urban thermal environment and alleviate urban heat island effect. Finally, after comparing the LST results in year 2000 and 2014, we find that the heat island phenomenon of the Taihu Lake region is increasing in the past 14 years and single existent urban heat islands has gradually become coexistent heat islands now. Building land expansion is the main reason that leads to the increasing of urban heat island.

Key words： Landsat-8;TIRS; ; atmospheric radiation transmission;SW;SC;Taihu Lake region

Cite this article

KAN Zeng-hui , LIU Chao-shun , LI Zhi-jun . Retrieval of land surface temperature based on Landsat-8 thermal infrared data and heat island effect analysis over the Taihu Lake region[J]. Journal of East China Normal University(Natural Science), 2016 , 2016(4) : 129 -138 . DOI: 10.3969/j.issn.1000-5641.2016.04.015

References

[ 1 ] 黄妙芬, 邢旭峰, 王培娟,等.利用LANDSAT/TM热红外通道反演地表温度的三种方法比较 [J]. 干旱区地理,2006, 29(1): 132-137.
[ 2 ] 覃志豪, ARNONKARNIELI. 用陆地卫星TM6数据演算地表温度的单窗算法 [J].地理学报, 2001, 56(4): 456-466.
[ 3 ] JIMENEZ-MUNOZ J C, SOBRINO J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data [J]. Journal of Geophysical Research, 2003, 108(D22): 2015-2023.
[ 4 ] ANDING D, KAUTH R. Estimation of sea surface temperature from space [J]. Remote Sensing of Environment, 1971, 1(4): 217-220.
[ 5 ] QIN Z, DALL’OLMO G, KARNIELI A, et al. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data [J]. Journal of Geophysical Research Atmospheres, 2001, 106(D19): 22655-22670.
[ 6 ] SOBRINO J A, LI Z L, STOLL M P, et al. Multi-channel and multi-angle algorithms for estimating sea and  land surface temperature with ATSR data [J]. International Journal of Remote Sensing, 1996, 17(11): 2089-2114.
[ 7 ] JIMENEZ-MUNOZ J C, SOBRINO J A. Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors [J]. IEEE Geoscience & Remote Sensing Letters, 2008, 5(4): 806-809.
[ 8 ] JIMENEZ-MUNOZ J C, SOBRINO J A, SKOKOVIC D, et al. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data [J]. IEEE Geoscience & Remote Sensing Letters, 2014, 11(10): 1840-1843.
[ 9 ] SNYDER W C, WANZ, ZHANGY, et al. Classification-based emissivity for land surface temperature measurement from space [J]. International Journal of Remote Sensing, 1998, 19(14): 2753-2774.
[10] VAN DE GRIENDAA, OWE M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces [J]. International Journal of Remote Sensing, 1993, 84(3): 1119-1131.
[11] QIN Z, GAO M. An algorithm to retrieve land surface temperature from ASTER thermal band data for  agricultural drought monitoring [J]. Proceedings of SPIE-The International Society for Optical Engineering, 2006, 6359. DOI: 10.1117/12.689307.
[12] 王国复, 李集明, 邓莉,等. 中国气象科学数据共享服务网总体设计与建设 [J].应用气象学报, 2004, 15(z1): 10-16.
[13] CATTRALL C, THOME K J. Exploitation of MODTRAN4 capabilities to predict at-sensor radiance [J]. Optical Spectroscopic Techniques & Instrumentation for Atmospheric & Space Research V, 2003, 5157:98-106.
[14] SOBRINO J A, JIM´ENEZ-MU˜ NOZ J C, PAOLINI L. Land surface temperature retrieval from LANDSAT TM 5 [J]. Remote Sensing of Environment, 2004, 90(4): 434-440.
[15] COLL C, CASELLES V, VALOR E, et al. Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data [J]. Remote Sensing of Environment, 2012, 117(1): 199-210.
[16] LO C P, QUATTROCHI D A, LUVALL J C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect [J]. International Journal of Remote Sensing, 1997, 18(2): 287-304.
[17] WILSON J S, CLAY M, MARTIN E, et al. Evaluating environmental influences of zoning in urban ecosystems with remote sensing [J]. Remote Sensing of Environment, 2003, 86(3): 303-321. 

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