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国土资源遥感  2018, Vol. 30 Issue (1): 45-53    DOI: 10.6046/gtzyyg.2018.01.07
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基于Landsat8数据的2种海表温度反演单窗算法对比——以红沿河核电基地海域为例
陈瀚阅1,2,3(), 朱利4, 李家国5, 范协裕1,2,3
1.福建农林大学资源与环境学院,福州 350002
2.福建农林大学土壤生态系统健康与调控福建省高校重点实验室,福州 350002
3.福建农林大学福建省土壤环境健康与调控重点实验室,福州 350002
4.环境保护部卫星环境应用中心,北京 100094
5.中国科学院遥感与数字地球研究所,北京 100101
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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摘要 

以辽宁省红沿河核电站附近海域为研究区,对单窗算法用于Landsat8 TIRS数据反演沿海海表温度(sea surface temperature,SST)的适用性进行比较分析。首先,基于大气廓线数据(thermodynamic initial guess retrieval,TIGR),针对 Landsat8 TIRS第10波段修订QK&B算法系数; 然后,从星地同步验证和参数敏感性2方面对辐射传输模型(radiation transfer model,RTM)和QK&B算法进行对比分析。结果表明,结合美国国家环境预报中心(National Centers for Environmental Prediction,NCEP) 大气参数实现的RTM算法精度较QK&B算法略高; 对于QK&B算法,基于NCEP廓线数据模拟的大气透过率比经验方程估算的值更具优势; RTM算法对大气透过率敏感性相对较高,且明显高于QK&B算法; 而 QK&B算法对大气平均作用温度的敏感性较高; RTM算法对大气透过率、大气上行辐射以及QK&B算法对大气透过率、大气平均作用温度的敏感性均随着水汽含量的增加而增大。

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关键词 海表温度Landsat8单窗算法敏感性分析热红外遥感    
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.

Key wordssea surface temperature    Landsat8    mono-window algorithm    sensitivity analysis    thermal infrared remote sensing
收稿日期: 2016-07-21      出版日期: 2018-02-08
:  TP79  
基金资助:福建省教育厅科技项目“多角度遥感反演作物叶面积指数方法研究”(编号: JA14126)、国家自然科学基金青年项目“基于各向异性角度指数的作物叶面积指数遥感模型研究”(编号: 41401399)和中国科学院数字地球重点实验室开放基金项目“多角度遥感反演作物叶面积指数方法研究”(编号: 2014LDE008)共同资助
作者简介:

第一作者: 陈瀚阅(1985-),女,博士研究生,讲师,主要从事热红外遥感植被结构参数反演研究。Email:chenhanyue.420@163.com

引用本文:   
陈瀚阅, 朱利, 李家国, 范协裕. 基于Landsat8数据的2种海表温度反演单窗算法对比——以红沿河核电基地海域为例[J]. 国土资源遥感, 2018, 30(1): 45-53.
Hanyue CHEN, Li ZHU, Jiaguo LI, Xieyu FAN. A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station. Remote Sensing for Land & Resources, 2018, 30(1): 45-53.
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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.01.07      或      https://www.gtzyyg.com/CN/Y2018/V30/I1/45
Fig.1  走航式测量航线验证点分布
Fig.2  Landsat8 TIRS_B10 τω关系拟合
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  Landsat8 TIRS_B10 τ估算方程
Fig.3  TaT1近地表空气温度关系拟合
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估算方程
Fig.4  TIRS_B10数据SST分布结果
Fig.5  算法绝对误差对比
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  SST反演结果精度评价
Fig.6  算法反演结果与实测结果对比
Fig.7  RTM算法对4种参数误差敏感性分析
Fig.8  QK&B算法对3种参数误差敏感性分析
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