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Landsat-8地表温度反演及其与MODIS温度产品的对比分析

张爱因 张晓丽

张爱因, 张晓丽. Landsat-8地表温度反演及其与MODIS温度产品的对比分析[J]. 北京林业大学学报, 2019, 41(3): 1-13. doi: 10.13332/j.1000-1522.20180234
引用本文: 张爱因, 张晓丽. Landsat-8地表温度反演及其与MODIS温度产品的对比分析[J]. 北京林业大学学报, 2019, 41(3): 1-13. doi: 10.13332/j.1000-1522.20180234
Zhang Aiyin, Zhang Xiaoli. Land surface temperature retrieved from Landsat-8 and comparison with MODIS temperature product[J]. Journal of Beijing Forestry University, 2019, 41(3): 1-13. doi: 10.13332/j.1000-1522.20180234
Citation: Zhang Aiyin, Zhang Xiaoli. Land surface temperature retrieved from Landsat-8 and comparison with MODIS temperature product[J]. Journal of Beijing Forestry University, 2019, 41(3): 1-13. doi: 10.13332/j.1000-1522.20180234

Landsat-8地表温度反演及其与MODIS温度产品的对比分析

doi: 10.13332/j.1000-1522.20180234
基金项目: 国家重点研发计划项目(2017YFD0600902)
详细信息
    作者简介:

    张爱因。主要研究方向:定量遥感。Email:aiiyinzhang@gmail.com 地址:香港九龙红磡湾育才道11号香港理工大学土地测量与地理资讯学系

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:林业遥感与信息技术。Email:zhang-xl@263.net 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

  • 中图分类号: S771.8;TP72

Land surface temperature retrieved from Landsat-8 and comparison with MODIS temperature product

  • 摘要: 目的地表温度是区域与全球尺度地表过程分析与模拟的重要参数,在地表与大气能量交换的过程中扮演着重要的角色。本文使用3种算法对北京地区Landsat-8影像进行地表温度反演,并使用MODIS 地表温度产品对反演结果进行交叉验证,评估Landsat-8用于地表温度反演的精度与适用性,为后续使用Landsat-8反演地表温度的研究提供参考。方法对反演地表温度所需的3个重要参数(大气平均作用温度、地表比辐射率、水汽含量)进行获取,得到3个算法的反演结果后,对各个算法的结果进行敏感性和差值分析,并将结果与同期MOD11A1地表温度产品进行对比分析。主要分析手段包括北京市不同行政区之间算法结果与温度产品的平均温度结果比较、不同地类之间算法结果与温度产品的平均温度结果比较,以及选取尺度效应较低、温度随时间变化较小的密云水库中心区域比较两者的温差。结果3个算法的反演结果总体平均温差不超过1 K,其中Cristóbal等提出的改进后单通道算法与其他两个算法的温差最小,Wang等提出的改进后单窗算法与其他两个算法的温差最大。Landsat-8地表温度反演结果普遍高于MODIS温度产品。通过选取密云水库中心区域对本研究反演结果和温度产品进行对比可以得出,3种算法结果与MODIS温度产品的总平均差值为1.373 K。结论反演结果总体上具有较理想的反演精度。Jiménez-Muñoz提出的劈窗算法具有最好的敏感性分析结果,且与MODIS温度产品的结果最接近。Landsat-8反演结果与MODIS温度产品在总体地温分布规律上保持一致,但Landsat-8因具有更高的分辨率而能更好地分辨小地块不同地类的地温差异,在精确反演地表温度领域拥有更大的优势。

     

  • 图  1  水汽含量敏感性分析结果散点与趋势线模拟示意图

    (a)IMW算法水汽含量敏感性分析结果Results of sensitivity analysis by water vapor contents of IMW algorithm;(b)ISC算法水汽含量敏感性分析结果Results of sensitivity analysis by water vapor contents of ISC algorithm;(c)JSW算法水汽含量敏感性分析结果Results of sensitivity analysis by water vapor contents of JSW algorithm. LST:地表温度Land surface temperature

    Figure  1.  Scatter and trend line diagram from the results of sensitivity analysis by water vapor contents

    图  2  地表比辐射率敏感性分析结果散点与趋势线模拟示意图

    (a)IMW算法地表比辐射率敏感性分析结果Results of sensitivity analysis by LSE of IMW algorithm;(b)ISC算法地表比辐射率敏感性分析结果Results of sensitivity analysis by LSE of ISC algorithm;(c)JSW算法地表比辐射率敏感性分析结Results of sensitivity analysis by LSE of JSW algorithm. LSE:地表比辐射率Land surface emissivity

    Figure  2.  Scatter and trend line diagram from the results of sensitivity analysis by ground emissivity

    图  3  3种算法Landsat-8反演结果分布图

    Figure  3.  Distribution map of retrieval results from Landsat-8 data

    图  4  Landsat-8的3种算法与MODIS温度产品北京分区平均温度对比

    Figure  4.  Comparison between mean LST retrieved by 3 algorithms of Landsat-8 data and MODIS temperature product in Beijing Region

    图  5  不同地类Landsat-8反演结果与MODIS温度产品对比图

    Figure  5.  Comparison between retrieval results from Landsat-8 and MODIS temperature product by different ground feature

    图  6  北京局部地区Landsat-8 ISC算法反演结果与MODIS温度产品地表温度分布图对比(2017年9月12日)

    Figure  6.  LST distribution map of Landsat-8 ISC algorithm retrieval results and MODIS temperature product in Beijing local area (September 12, 2017)

    表  1  大气平均作用温度(Ta)与近地表温度(T0)线性估计方程式

    Table  1.   Linear relations for the approximation of effective mean atmospheric temperature (Ta) from the near surface air temperature (T0)

    大气模式 Atmosphere model线性关系式 Linear relation
    中纬度夏季 Mid-latitude summerTa = 16.011 0 + 0.926 2 T0
    热带大气 Tropical atmosphere modelTa = 17.976 9 + 0.917 2 T0
    中纬度冬季 Mid-latitude winterTa = 19.270 4 + 0.911 2 T0
    下载: 导出CSV

    表  2  典型地类在Landsat-8 TIRS波段的地表比辐射率值

    Table  2.   Land surface emissivity of representative ground features for Landsat-8 TIRS

    地表类型 Ground feature水域 Water area建筑 Building裸土 Bare soil植被 Vegetation
    10波段比辐射率值 Emissivity in band 100.9910.9620.9660.972
    11波段比辐射率值 Emissivity in band 110.9860.9630.9700.973
    下载: 导出CSV

    表  3  Landsat-8 TIRS波段大气透过率估算方程

    Table  3.   Estimation of atmospheric transmittance for the Landsat 8 TIRS bands

    大气模式 Atmosphere model水汽含量
    Water vapor content/(g·cm− 2)
    大气透过率估算方程
    Transmittance estimating equation
    R2SEE
    中纬度夏季 Mid-latitude summer0.2 ~ 1.6τ10 = 0.918 4 − 0.072 5 w0.9830.004 3
    1.6 ~ 4.4τ10 = 1.016 3 − 0.133 0 w0.9990.003 3
    4.4 ~ 5.4τ10 = 0.702 9 − 0.062 0 w0.9660.008 1
    热带大气 Tropical atmosphere model0.2 ~ 2.0τ10 = 0.922 0 − 0.078 0 w0.9830.005 9
    2.0 ~ 5.6τ10 = 1.022 2 − 0.131 0 w0.9990.003 3
    5.6 ~ 6.8τ10 = 0.542 2 − 0.044 0 w0.9910.001 7
    中纬度冬季 Mid-latitude winter0.2 ~ 1.4τ10 = 0.922 8 − 0.073 5 w0.9880.003 3
    注:SEE为标准估计误差,τ10为大气透过率,w为水汽含量。Notes: SEE means standard estimation error, τ10 means atmospheric transmittance, w means water vapor content.
    下载: 导出CSV

    表  4  ${\psi_1}$${\psi_2}$${\psi_3}$计算系数

    Table  4.   Numerical coefficients for ${\psi_1}$, ${\psi_2}$, and ${\psi_3}$

    系数 Coefficientψ1ψ2ψ3
    a4.472 973 036− 30.370 278 530− 3.761 839 863
    b− 0.000 074 8260.000 911 877− 0.000 141 775
    c0.046 628 212− 0.573 195 6710.091 136 221
    d0.023 169 178− 0.784 441 9530.545 348 754
    e− 4.961 73×10− 50.001 408 070− 0.000 909 502
    f− 0.026 274 5280.215 779 7230.041 809 016
    g− 2.452 320 564106.550 930 400− 79.958 380 610
    h0.000 376 021− 0.000 376 021− 0.000 104 728
    i− 7.212 197 93889.615 688 890− 14.659 549 110
    下载: 导出CSV

    表  5  3种算法反演得到的北京地区平均地温

    Table  5.   Average LST of Beijing region retrieved by 3 different algorithms K

    算法 Algorithm日期 Date
    2017−03−042017−05−072017−07−102017−09−122017−11−15
    IMW287.84309.41308.88302.87280.84
    ISC286.54308.27309.22302.51280.30
    JSW286.58308.31308.73302.13279.30
    注:IMW、ISC、JSW分别代表Wang和Qin改进后单窗算法、Cristóbal和Jiménez-Muñoz改进后单通道算法、Jiménez-Muñoz劈窗算法。Notes: IMW, ISC, JSW represent Improved Mono-Window Algorithm, Improved Single-Channel Method and Jimenez-Munoz Split Window Algorithm, respectively.
    下载: 导出CSV

    表  6  3种算法之间反演结果差值比较

    Table  6.   Difference of the retrieval results between 3 algorithms K

    日期 DateIMW−ISCIMW−JSWISC−JSW总平均差值
    Total average
    difference
    总平均
    标准偏差
    Total average standard
    deviation
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviations
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviation
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviation
    2017−03−041.2950.3341.2530.721− 0.0390.6540.8620.570
    2017−05−071.1340.4301.0900.790− 0.0390.8090.7540.676
    2017−07−10− 0.3370.4710.1480.7860.4870.7360.7360.664
    2017−09−120.3570.2510.7340.6180.3820.5130.4910.461
    2017−11−150.5380.3561.5200.5000.9880.5401.0150.465
    绝对值的平均值
    Absolute total average
    0.732 20.368 40.9490.6830.3870.650 40.6340.567
    下载: 导出CSV

    表  7  Landsat-8算法反演结果与MODIS温度产品平均温差(密云水库地区)

    Table  7.   Mean temperature difference between retrieved results from Landsat-8 and MODIS temperature product (Miyun Reservoir Area) K

    日期
    Date
    IMW−MODISISC−MODISJSW−MODIS总平均差值
    Total average
    difference
    总平均标准偏差
    Total average standard
    deviation
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviation
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviation
    算法
    温差值
    LST difference
    差值标
    准偏差
    Standard deviation
    2017−03−04− 2.4531.556− 2.7911.521− 2.8261.388− 2.6901.488
    2017−05−07− 1.6530.708− 1.4491.429− 2.4351.495− 1.8461.211
    2017−07−10− 0.3000.6550.8900.6530.2330.6490.2740.652
    2017−09−12− 0.2690.244− 0.2320.202− 0.2440.284− 0.2480.243
    2017−11−152.4200.5431.5140.5230.8930.5661.6090.544
    绝对值的平均值
    Absolute total average
    1.419 00.741 21.375 00.865 61.326 00.876 41.373 00.828 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-19
  • 修回日期:  2018-11-27
  • 网络出版日期:  2019-03-28
  • 刊出日期:  2019-03-01

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