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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (1) : 45-53     DOI: 10.6046/gtzyyg.2018.01.07
Orginal Article |
A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station
Hanyue CHEN1,2,3(), Li ZHU4, Jiaguo LI5, Xieyu FAN1,2,3
1. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. Key Laboratory of Soil Ecosystem Health and Regulation in Fujian Higher Education, Fujian Agriculture and Forestry University, Fuzhou 350002, China
3. Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, Fujian Agriculture and Forestry University, Fuzhou 350002, China
4. Satellite Environment Center, Ministry of Environment Protection, Beijing 100094, China
5. Institute of Remote Sensing and Digital Earth Applications, Chinese Academy of Sciences, Beijing 100101, China
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Abstract  

Two mono-window algorithms, i.e., radiation transfer model (RTM) and QK&B algorithm, were evaluated and compared for their performance on sea surface temperature (SST) calculation from Landsat8/TIRS data in coastal water of Hongyan River nuclear power station. The parameters of QK&B algorithm were modified for Landsat8 thermal infrared band 10 based on thermodynamic initial guess retrieval (TIGR) atmospheric profile data, and both atmospheric transmittance values obtained from water vapor content based on empirical model and from National Centers for Environmental Prediction (NCEP) data were employed for QK&B algorithm respectively with the purpose of comparing their feasibilities for SST retrieval. A validation with shipboard measurements of SST collected synchronically shows that the slightly better accuracy in SST retrieval is observed from RTM method (RMSE = 0.525 0) than from modified QK&B algorithm (RMSE = 0.638 0). QK&B algorithm using atmospheric transmittance simulated using NCEP data provided better accuracy than that using atmospheric transmittance estimated from water vapor content. Sensitivity analysis based on data simulated by MODTRAN4.0 using NCEP data was conducted. The results show that atmospheric transmittance has the greatest impact on the accuracy of SST retrieval among all parameters input RTM method, followed by atmospheric upward radiation. Atmospheric transmittance also shows greater sensitivity for RTM method than that for QK&B algorithm. For QK&B algorithm, atmospheric average temperature has greater impact on SST retrieval than other parameters input. Atmospheric upward radiation for RTM method, atmospheric average temperature for QK&B algorithm and atmospheric transmittance for both two algorithms show increasing impact on SST retrieval with increasing water vapor content.
Keywords sea surface temperature      Landsat8      mono-window algorithm      sensitivity analysis      thermal infrared remote sensing     
:  TP79  
Issue Date: 08 February 2018
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Hanyue CHEN
Li ZHU
Jiaguo LI
Xieyu FAN
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Hanyue CHEN,Li ZHU,Jiaguo LI, et al. A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station[J]. Remote Sensing for Land & Resources, 2018, 30(1): 45-53.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.07     OR     https://www.gtzyyg.com/EN/Y2018/V30/I1/45
Fig.1  Distribution of validation points
Fig.2  Fitted curves of τ and ω of Landsat8 TIRS_B10
TIGR τ估算方程 R2 RMSE
中纬度夏季 τ=0.001 4ω3-0.009 5ω3-0.098 9ω+0.985 7 0.971 3 0.013 1
中纬度冬季 τ=0.002 1ω3-0.015 1ω3-0.089 6ω+0.981 0 0.966 1 0.008 0
中纬度综合 τ=0.001 9ω3-0.012 6ω3-0.093 6ω+0.982 8 0.964 4 0.011 9
Tab.1  τ estimating equation for Landsat8 TIRS_B10
Fig.3  Fitted curves of Ta and T1
TIGR Ta估算方程 R2
中纬度夏季 Ta=0.711 4T1+73.662 0 0.864 1
中纬度冬季 Ta=0.660 6T1+85.171 0 0.780 9
中纬度综合 Ta=0.739 1T1+65.068 0 0.850 9
Tab.2  Ta estimating equation
Fig.4  Distributions of SST derived from TIRS_B10
Fig.5  Comparison of absolute error for different algorithms
评价指标 RTM QK&B1 QK&B2
平均误差 0.414 0.532 1.078
最大误差 1.751 1.884 0.828
最小误差 -0.885 -1.058 -3.235
RMSE 0.525 0.638 1.257
Tab.3  Accuracy of SST(℃)
Fig.6  Comparison between SST retrieved by algorithm and shipboard measurement of SST
Fig.7  Sensitivity analysis results of four parameters employed in RTM algorithm
Fig.8  Sensitivity analysis results of three parameters employed in QK&B algorithm
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