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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 228-236     DOI: 10.6046/gtzyyg.2020188
Spatio-temporal variation of land surface temperature and land cover responses in different seasons in Shengjin Lake wetland during 2000—2019 based on Google Earth Engine
YE Wantong1(), CHEN Yihong1, LU Yinhao1,2, Wu Penghai1,2,3()
1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. Key Laboratory of Ecological Protection and Restoration of Wetland in Anhui Province, Anhui University, Hefei 230601, China
3. Institute of Physical Science and Information Technology, Anhui University, Hefei 230601, China
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In order to better carry out the ecological protection and restoration of the wetland in the lower reaches of the Yangtze River, the authors selected the Landsat images of Shengjin Lake in different seasons from 2000 to 2019 as the research data with the support of Google Earth Engine (GEE) cloud platform. The land surface temperature (LST) was retrieved by a batch program using radiative transfer equation method. The spatio-temporal variation of LST and its responses to land cover in Shengjin Lake during the past 20 years were comprehensively analyzed. The results are as follows: ① From the perspective of space, the spatial distribution of different temperature grades has shown obvious differences with the seasonal changes. The high-temperature region is dispersed in spring, generally located in the northwest in summer and autumn, and mostly in the south in winter. The area of the lake varies with the seasons, but its temperature belongs to very low or low temperature grade at all seasons. ② From the perspective of time, in the past 20 years, affected by forest and water, the Shengjin Lake wetland has always been dominated by the medium and low temperature grades that account for a large proportion of 70%-85% or so. The area proportion of temperature grades varies with the time trend such as seasons and years. ③ There exist seasonal differences in the responses of LST to land cover. It is basically presented in the form of a descending order of artificial surface> cultivated land> forest and mudflat> water. ④ Non-urbanization factors have a certain impact on the surface temperature of natural wetland. The research results are certainly significant for the reasonable development of Shengjin Lake.

Keywords Google Earth Engine      Landsat      land surface temperature      spatio-temporal variation      Shengjin Lake     
ZTFLH:  TP79  
Corresponding Authors: Wu Penghai     E-mail:;
Issue Date: 21 July 2021
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Wantong YE
Yihong CHEN
Yinhao LU
Penghai Wu
Cite this article:   
Wantong YE,Yihong CHEN,Yinhao LU, et al. Spatio-temporal variation of land surface temperature and land cover responses in different seasons in Shengjin Lake wetland during 2000—2019 based on Google Earth Engine[J]. Remote Sensing for Land & Resources, 2021, 33(2): 228-236.
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Fig.1  The geographical location of study area
季节 传感器类型 获取时间
TM 2004-04-19,2005-03-05,2007-03-27,2008-05-16
ETM+ 2000-04-16,2001-05-21,2002-05-24,2003-03-08,2006-05-19,2009-04-09,2010-03-11,2011-03-30,2017-03-14
OLI/TIRS 2013-04-02,2014-05-01,2019-03-12
TM 2004-07-24,2005-08-12,2006-07-30,2009-06-04
ETM+ 2002-07-11,2003-07-30,2008-07-27,2010-08-18,2012-07-22,2018-08-24
OLI/TIRS 2016-07-25,2017-07-28,2019-08-19
TM 2001-10-20,2003-10-26,2004-10-12,2005-09-13,2007-10-05,2009-10-26
ETM+ 2000-10-09,2002-09-29,2006-09-24,2008-10-15,2010-10-05,2017-10-24,2019-09-28
OLI/TIRS 2013-10-05,2014-10-08,2015-10-11,2016-09-27,2018-10-03
TM 2000-11-02,2001-11-21,2002-11-08,2004-12-31,2005-10-31,2006-12-21,2008-12-10
ETM+ 2003-12-21,2007-11-30,2009-12-21,2010-12-08,2011-12-11,2012-11-11,2015-01-04,2016-02-08
OLI/TIRS 2016-12-16,2017-12-19,2019-01-23,2019-11-23
Tab.1  The remote sensing data of Landsat
日期 总体分类
Kappa系数 日期 总体分类
Kappa系数 日期 总体分类
2002-05-24 93.49 0.906 6 2010-03-11 95.92 0.935 6 2019-03-12 92.32 0.891 5
2002-07-11 94.50 0.916 0 2010-08-18 98.07 0.952 0 2019-08-19 94.31 0.921 3
2002-09-29 95.52 0.934 2 2010-10-05 96.75 0.953 1 2019-09-28 90.64 0.872 4
2002-11-08 92.50 0.889 8 2010-12-08 96.87 0.956 6 2019-11-23 92.83 0.899 5
Tab.2  The evaluation results of classification accuracy
等级 分级标准
极低温 TTmean-1.5STD
低温 Tmean-1.5STD<TTmean-STD
中温 Tmean-STD<TTmean+STD
高温 Tmean+STD<TTmean+1.5STD
极高温 T>Tmean+1.5STD
Tab.3  The classification of land surface temperature based on mean and standard deviation
Fig.2  Spatial distribution of land surface temperature grades average at all seasons during 20 years
Fig.3  Proportion of different temperature grades at all seasons from 2000 to 2019
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