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大气校正模型对多光谱水深反演影响的多维度分析

张焕炜 马毅 张靖宇

张焕炜,马毅,张靖宇. 大气校正模型对多光谱水深反演影响的多维度分析[J]. 海洋学报,2022,44(7):1–16 doi: 10.12284/hyxb2022122
引用本文: 张焕炜,马毅,张靖宇. 大气校正模型对多光谱水深反演影响的多维度分析[J]. 海洋学报,2022,44(7):1–16 doi: 10.12284/hyxb2022122
Zhang Huanwei,Ma Yi,Zhang Jingyu. Multi-dimensional analysis of atmospheric correction models on multi-spectral water depth inversion[J]. Haiyang Xuebao,2022, 44(7):1–16 doi: 10.12284/hyxb2022122
Citation: Zhang Huanwei,Ma Yi,Zhang Jingyu. Multi-dimensional analysis of atmospheric correction models on multi-spectral water depth inversion[J]. Haiyang Xuebao,2022, 44(7):1–16 doi: 10.12284/hyxb2022122

大气校正模型对多光谱水深反演影响的多维度分析

doi: 10.12284/hyxb2022122
基金项目: 国家自然科学基金重点项目(51839002);国家自然科学基金青年项目(41906158);高分海洋资源环境遥感信息处理与业务应用示范系统国防科工局高分专项(41-Y30F07-9001-20/22)。
详细信息
    作者简介:

    张焕炜(1998-),女,河南省安阳市人,主要从事海洋遥感与应用研究。E-mail: huanwei98@163.com

    通讯作者:

    马毅,研究员,主要从事海岛海岸带遥感与应用研究。E-mail: mayimail@fio.org.cn

  • 中图分类号: TP79

Multi-dimensional analysis of atmospheric correction models on multi-spectral water depth inversion

  • 摘要: 大气校正是水体定量遥感的基础与前提。本文从大气校正模型、大气校正模型参数、水体组分差异以及水深反演波段组合方式4个维度探讨大气校正模型对水深反演的影响。研究采用6S、FLAASH、ACOLITE与QUAC 4种大气校正模型,选取大陆型、海洋型与城市型气溶胶模式,以瓦胡岛西北侧与谢米亚岛周边浅水作为清洁水体研究区,以辽东浅滩与槟城海峡作为浑浊水体研究区,基于Landsat-8多光谱影像开展大气校正,并采用8种波段组合方式进行水深遥感反演。研究结果表明:(1)4种大气校正模型均可在一定程度上削弱大气对水体信号的影响;因参数选取以及研究区水体组分的不同,不同模型的校正结果存在一定差异;两类水体反射率峰值分别出现在蓝波段与绿波段。(2)6S大气校正模型鲁棒性较强,该模型因研究区水体组分发生变化导致对应的水深反演结果与其余模型相比波动较小;FLAASH模型在海洋型和城市型两种气溶胶模式水深反演结果在浑浊水体存在较为明显的差异,辽东浅滩浅水区平均相对误差相差7.9%;ACOLITE模型受水体类型影响显著且对浑浊水体具有优越性与稳定性,平均相对误差较FLAASH降低5.6%。(3)多波段水深反演精度普遍优于单波段,但反演精度与波段数目之间无显著的相关性;水深反演波段组合方式对不同研究区敏感性不同,清洁水体三波段模型的反演精度较好,浑浊水体中四波段模型的反演精度最优,平均相对误差较三波段模型降低达5.6%。
  • 图  1  清洁水体遥感影像与水深点分布

    Fig.  1  Remote sensing image of clean water and distribution of water depth points

    图  2  浑浊水体遥感影像与水深点分布

    Fig.  2  Remote sensing image of turbid water and distribution of water depth points

    图  3  清洁水体4个波段校正结果

    Fig.  3  Clean water four bands atmospheric correction results

    图  4  浑浊水体4个波段校正结果

    Fig.  4  Turbid water four bands atmospheric correction results

    图  5  瓦胡岛检验区蓝波段反射率分布

    Fig.  5  Blue-band reflectance distribution in the Oahu Island test area

    图  6  辽东浅滩检验区绿波段反射率分布

    Fig.  6  Green-band reflectance distribution in the Liaodong shoal test area

    图  7  瓦胡岛不同波段组合模型水深反演结果精度分析

    Fig.  7  Accuracy analysis of bathymetric inversion results of different band combination models of Oahu Island

    图  8  辽东浅滩不同波段组合模型水深反演结果精度分析

    Fig.  8  Accuracy analysis of bathymetric inversion results of different band combination models of Liaodonng shoal

    图  9  瓦胡岛研究区不同大气校正模型反演水深值与参考水深值散点图

    Fig.  9  Scatter plots of bathymetry and reference bathymetry for different atmospheric correction models for Oahu Island

    图  10  辽东浅滩研究区不同大气校正模型反演水深值与参考水深值散点图

    Fig.  10  Scatter plots of bathymetry and reference bathymetry for different atmospheric correction models for Liaodong shoal

    表  1  清洁水体水深点分布

    Tab.  1  Distribution of water depth points in clean water

    水深点研究区整体0~5 m5~10 m10~15 m15~20 m
    控制点瓦胡岛18348514737
    谢米亚岛20244545153
    检查点瓦胡岛8922292414
    谢米亚岛9318232923
    下载: 导出CSV

    表  2  浑浊水体水深点分布

    Tab.  2  Distribution of water depth points in turbid water

    水深点研究区整体0~5 m5~10 m10~15 m15~20 m
    控制点辽东浅滩21750745637
    槟城17850565220
    检查点辽东浅滩9518312719
    槟城8819272418
    下载: 导出CSV

    表  3  气溶胶模式4种基本粒子体积比

    Tab.  3  Volume ratio of four basic particles in aerosol model

    类型水溶性粒子类尘埃海洋性粒子烟尘性粒子
    大陆型0.290.700.01
    海洋型0.050.95
    城市型0.610.170.22
      注:− 代表没有该类型的物质。
    下载: 导出CSV

    表  4  大气校正参数

    Tab.  4  Atmospheric correction parameters

    研究区大气模式波长550 nm光学厚度平均高程/km
    瓦胡岛热带0.0780.07
    谢米亚岛中纬度夏季0.1850.00
    辽东浅滩中纬度夏季0.0780.01
    槟城热带0.2380.05
    下载: 导出CSV

    表  5  清洁水体校正结果标准差与变异系数

    Tab.  5  Standard deviation and coefficient of variation of atmospheric correction results for clean water

    中心波长/nm瓦胡岛研究区 谢米亚岛研究区
    SDCV/10−3SDCV/10−3
    482.555.81116.7 56.53328.0
    562.537.80148.634.31350.3
    65528.41182.717.86358.5
    86522.73149.728.80585.1
    下载: 导出CSV

    表  6  浑浊水体校正结果标准差与变异系数

    Tab.  6  Standard deviation and coefficient of variation of atmospheric correction results for turbid water

    中心波长/nm辽东浅滩研究区 槟城研究区
    SDCV/10−3SDCV/10−3
    482.574.96116.7 147.35159.7
    562.599.19114.7122.16126.0
    65560.5678.964.91114.3
    86518.72128.359.80242.0
    下载: 导出CSV

    表  7  不同波段组合模型

    Tab.  7  Different band combination model

    波段数目组合方式
    单波段BGR
    双波段B+GB+RG+R
    三波段B+G+R
    四波段B+G+R+NIR
    下载: 导出CSV

    表  8  清洁水体不同波段组合模型水深反演结果精度

    Tab.  8  The accuracy of water depth inversion results of different band combination models for clean water

    研究区指标MAE平均值/m MRE平均值/%
    瓦胡岛模型GB+GB+G+RB+G+R+NIR GB+GB+G+RB+G+R+NIR
    6S大陆型2.501.711.431.4748.326.922.423.5
    6S城市型2.521.731.441.4749.027.522.523.6
    6S海洋型2.461.651.431.4947.225.722.123.3
    FLAASH城市型2.681.721.411.4451.326.722.623.5
    FLAASH海洋型2.671.711.431.4549.725.122.222.5
    ACOLITE2.282.081.911.9141.733.832.333.2
    QUAC2.491.661.421.4748.125.521.923.5
    谢米亚岛模型GB+GB+G+RB+G+R+NIRGB+GB+G+RB+G+R+NIR
    6S大陆型2.432.432.472.4350.832.428.628.9
    6S城市型3.592.722.492.4750.433.428.729.4
    6S海洋型3.592.722.512.4050.633.029.328.7
    FLAASH城市型3.922.922.562.5458.434.829.330.4
    FLAASH海洋型4.103.142.422.4262.838.827.728.1
    ACOLITE3.672.632.322.3452.730.825.726.9
    QUAC3.792.702.342.3655.932.927.930.4
      注:本表中展示了精度较好的几种波段组合方式。
    下载: 导出CSV

    表  9  清洁水体不同波段组合模型水深反演结果均值与标准差

    Tab.  9  Mean and standard deviation of bathymetric inversion results of different band combination models for clean water

    研究区指标BGRB+GB+RG+RB+G+RB+G+R+NIR
    瓦胡岛MAE平均值/m 3.74 2.51 3.67 1.75 3.75 2.34 1.50 1.53
    标准差/m 0.11 0.14 0.12 0.15 0.11 0.07 0.18 0.17
    MRE平均值/%68.2 47.9 60.4 27.3 59.3 44.4 23.7 24.7
    标准差/%3.83.03.23.03.80.83.83.8
    谢米
    亚岛
    MAE平均值/m 4.14 3.59 3.91 2.75 4.08 3.60 2.44 2.42
    标准差/m 0.69 0.50 0.62 0.21 0.67 0.49 0.08 0.06
    MRE平均值/%70.5 54.5 59.7 33.7 64.3 54.8 28.2 29.0
    标准差/%2.14.42.92.34.53.41.21.2
    下载: 导出CSV

    表  11  浑浊水体不同波段组合模型水深反演结果均值与标准差

    Tab.  11  Mean and standard deviation of bathymetric inversion results of different band combination models for turbid water

    研究区指标BGRB+GB+RG+RB+G+RB+G+R+NIR
    辽东浅滩MAE平均值/m3.823.973.913.443.793.893.332.91
    标准差/m0.010.010.010.030.010.000.070.16
    MRE平均值/%50.253.352.243.049.651.841.736.1
    标准差/%0.90.70.70.80.80.40.61.9
    槟城MAE平均值/m3.273.233.583.243.233.083.082.96
    标准差/m0.010.010.030.010.000.020.030.04
    MRE平均值/%43.841.350.742.143.338.638.436.4
    标准差/%0.20.30.40.30.30.50.80.6
    下载: 导出CSV

    表  10  浑浊水体不同波段组合模型水深反演结果精度

    Tab.  10  The accuracy of water depth inversion results of different band combination models for turbid water

    研究区指标MAE/mMRE/%
    辽东浅滩模型BB+GB+G+RB+G+R+NIRBB+GB+G+RB+G+R+NIR
    6S大陆型3.833.423.272.8150.943.441.435.2
    6S城市型3.833.443.302.8250.943.741.835.2
    6S海洋型3.833.423.272.8150.943.741.535.1
    FLAASH城市型3.813.473.413.0449.142.141.837.6
    FLAASH海洋型3.813.453.383.2049.241.741.539.7
    ACOLITE3.823.403.262.7750.943.240.934.1
    QUAC3.813.483.402.9449.343.142.835.9
    槟城模型GG+RB+G+RB+G+R+NIRGG+RB+G+RB+G+R+NIR
    6S大陆型3.233.073.062.9441.238.237.836.1
    6S城市型3.223.073.052.9441.238.237.736.0
    6S海洋型3.233.063.052.9441.238.137.635.9
    FLAASH城市型3.213.123.123.0441.339.339.537.4
    FLAASH海洋型3.223.113.113.0341.339.139.237.2
    ACOLITE3.233.063.042.9441.238.237.736.0
    QUAC3.253.093.102.9242.039.239.436.2
      注:本表只展示了精度较好的几种波段组合方式。
    下载: 导出CSV

    表  12  清洁水体分段水深精度评价

    Tab.  12  Accuracy evaluation of segmented depth of clean water

    研究区水深段/m指标6S大陆型6S城市型6S海洋型FLAASH城市型FLAASH海洋型ACOLITEQUAC
    瓦胡岛0~5MAE/m1.161.171.151.181.201.931.20
    MRE/%44.644.744.045.245.074.344.7
    5~10MAE/m1.131.151.131.151.111.651.20
    MRE/%16.216.416.116.816.223.317.0
    10~15MAE/m1.511.521.531.421.411.821.67
    MRE/%13.013.013.112.112.015.414.2
    15~20MAE/m1.831.841.811.711.852.531.77
    MRE/%9.89.99.79.110.013.99.5
    谢米
    亚岛
    0~5MAE/m2.142.212.192.281.911.741.72
    MRE/%62.263.165.164.762.149.959.8
    5~10MAE/m1.711.691.691.641.391.671.61
    MRE/%24.023.923.922.719.023.322.9
    10~15MAE/m1.911.861.942.001.931.911.90
    MRE/%14.714.214.915.314.814.814.8
    15~20MAE/m4.194.294.284.434.463.944.10
    MRE/%24.324.924.925.725.822.924.4
    下载: 导出CSV

    表  13  浑浊水体分段水深精度

    Tab.  13  Accuracy of segmented depth of turbid water

    研究区水深段/m指标6S大陆型6S城市型6S海洋型FLAASH城市型FLAASH海洋型ACOLITEQUAC
    辽东
    浅滩
    0~5MAE/m3.093.073.163.233.432.983.37
    MRE/%86.485.888.591.199.082.589.1
    5~10MAE/m2.042.032.132.072.101.931.88
    MRE/%27.327.328.627.628.625.725.2
    10~15MAE/m1.801.781.691.942.041.872.02
    MRE/%13.513.412.714.615.414.115.3
    15~20MAE/m5.295.325.205.355.725.225.56
    MRE/%30.630.730.030.832.730.132.0
    槟城0~5MAE/m1.761.751.761.721.731.751.80
    MRE/%65.164.364.666.366.364.565.7
    5~10MAE/m2.042.052.052.202.152.052.12
    MRE/%30.730.930.832.932.130.932.1
    10~15MAE/m2.262.252.252.392.402.252.13
    MRE/%18.418.318.419.619.718.317.3
    15~20MAE/m6.476.456.456.546.566.446.37
    MRE/%37.237.137.137.637.737.036.6
    下载: 导出CSV
  • [1] 王艳姣, 董文杰, 张培群, 等. 水深可见光遥感方法研究进展[J]. 海洋通报, 2007, 26(5): 92−101. doi: 10.3969/j.issn.1001-6392.2007.05.015

    Wang Yanjiao, Dong Wenjie, Zhang Peiqun, et al. Progress in water depth mapping from visible remote sensing data[J]. Marine Science Bulletin, 2007, 26(5): 92−101. doi: 10.3969/j.issn.1001-6392.2007.05.015
    [2] Renosh P R, Doxaran D, De Keukelaere L, et al. Evaluation of atmospheric correction algorithms for sentinel-2-MSI and sentinel-3-OLCI in highly turbid estuarine waters[J]. Remote Sensing, 2020, 12(8): 1285. doi: 10.3390/rs12081285
    [3] Gordon H R. Removal of atmospheric effects from satellite imagery of the oceans[J]. Applied Optics, 1978, 17(10): 1631−1636. doi: 10.1364/AO.17.001631
    [4] Gordon H R, Clark D K. Atmospheric effects in the remote sensing of phytoplankton pigments[J]. Boundary-Layer Meteorology, 1980, 18(3): 299−313. doi: 10.1007/BF00122026
    [5] Ruddick K G, Ovidio F, Rijkeboer M. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters[J]. Applied Optics, 2000, 39(6): 897−912. doi: 10.1364/AO.39.000897
    [6] 孔金玲, 杨晶, 孙晓明, 等. 多光谱遥感影像大气校正与悬沙浓度反演——以曹妃甸近岸海域为例[J]. 国土资源遥感, 2016, 28(3): 130−137.

    Kong Jinling, Yang Jing, Sun Xiaoming, et al. Atmospheric correction and suspended sediment concentration retrieval based on multi-spectral remote sensing images: a case study of Caofeidian offshore area[J]. Remote Sensing for Land & Resources, 2016, 28(3): 130−137.
    [7] 郑伟, 曾志远. 遥感图像大气校正方法综述[J]. 遥感信息, 2004(4): 66−70. doi: 10.3969/j.issn.1000-3177.2004.04.019

    Zheng Wei, Zeng Zhiyuan. A review on methods of atmospheric correction for remote sensing images[J]. Remote Sensing Information, 2004(4): 66−70. doi: 10.3969/j.issn.1000-3177.2004.04.019
    [8] Wang Hanghang, Wang Jie, Cui Yuhuan, et al. Consistency of suspended particulate matter concentration in turbid water retrieved from sentinel-2 MSI and landsat-8 OLI sensors[J]. Sensors, 2021, 21(5): 1662. doi: 10.3390/s21051662
    [9] 马毅, 张杰, 张靖宇, 等. 浅海水深光学遥感研究进展[J]. 海洋科学进展, 2018, 36(3): 331−351. doi: 10.3969/j.issn.1671-6647.2018.03.001

    Ma Yi, Zhang Jie, Zhang Jingyu, et al. Progress in shallow water depth mapping from optical remote sensing[J]. Advances in Marine Science, 2018, 36(3): 331−351. doi: 10.3969/j.issn.1671-6647.2018.03.001
    [10] Lyzenga D R. Passive remote sensing techniques for mapping water depth and bottom features[J]. Applied Optics, 1978, 17(3): 379−383. doi: 10.1364/AO.17.000379
    [11] 杨晓彤, 焦红波, 李艳雯, 等. 两种浅海水深快速反演方法对比研究[J]. 测绘科学, 2017, 42(11): 177−183.

    Yang Xiaotong, Jiao Hongbo, Li Yanwen, et al. Comparative research of two methods for fast waterdepthretrieval for shallow water[J]. Science of Surveying and Mapping, 2017, 42(11): 177−183.
    [12] 张鹰, 张东, 王艳姣, 等. 含沙水体水深遥感方法的研究[J]. 海洋学报, 2008, 30(1): 51−58.

    Zhang Ying, Zhang Dong, Wang Yanjiao, et al. Study of remote sensing-based bathymetric method in sand-containing water bodies[J]. Haiyang Xuebao, 2008, 30(1): 51−58.
    [13] 许海蓬, 张彦彦, 王磊, 等. 大气校正对水深遥感反演的影响分析[J]. 现代测绘, 2017, 40(3): 1−4, 9. doi: 10.3969/j.issn.1672-4097.2017.03.001

    Xu Haipeng, Zhang Yanyan, Wang Lei, et al. Influence analysis of atmospheric correction on bathymetry remote sensing inversion[J]. Modern Surveying and Mapping, 2017, 40(3): 1−4, 9. doi: 10.3969/j.issn.1672-4097.2017.03.001
    [14] 张彦彦, 许海蓬, 吴涛, 等. 不同波段数目及组合对水深反演的影响[J]. 江苏海洋大学学报(自然科学版), 2020, 29(2): 45−49.

    Zhang Yanyan, Xu Haipeng, Wu Tao, et al. The influence of different band number and combination on bathymetric inversion[J]. Journal of Jiangsu Ocean University (Natural Sciences Edition), 2020, 29(2): 45−49.
    [15] Penny P, Kathryn S, Holly B. United States Coast Pilot[M]. United States: National Oceanic and Atmospheric Administration, 2014, 32: 385−386.
    [16] 田震, 马毅, 张靖宇, 等. 基于Landsat-8遥感影像和LiDAR测深数据的水深主被动遥感反演研究[J]. 海洋技术学报, 2015, 34(2): 1−8.

    Tian Zhen, Ma Yi, Zhang Jingyu, et al. Study on the bathymetry inversion by active and passive remote sensing with Landsat-8 images and LIDAR data[J]. Journal of Ocean Technology, 2015, 34(2): 1−8.
    [17] 金玉休, 曹志敏, 吴建政, 等. 辽东浅滩潮流运动特征与沉积物输运[J]. 海洋地质与第四纪地质, 2015, 35(6): 33−40.

    Jin Yuxiu, Cao Zhimin, Wu Jianzheng, et al. Tidal current movement and its bearing on sediment transportation on Liaodong shoal[J]. Marine Geology & Quaternary Geology, 2015, 35(6): 33−40.
    [18] Vanhellemont Q, Ruddick K. Turbid wakes associated with offshore wind turbines observed with Landsat 8[J]. Remote Sensing of Environment, 2014, 145: 105−115. doi: 10.1016/j.rse.2014.01.009
    [19] Martins V S, Barbosa C C F, De Carvalho L A S, et al. Assessment of atmospheric correction methods for sentinel-2 MSI images applied to amazon floodplain lakes[J]. Remote Sensing, 2017, 9(4): 322. doi: 10.3390/rs9040322
    [20] Shen Junjie, Jiang Jie, Du Yixi, et al. Impact of aerosol type on atmospheric correction of case II waters[J]. IOP Conference Series: Earth and Environmental Science, 2019, 234(1): 012019.
    [21] Vermote E F, Tanre D, Deuze J L, et al. Second simulation of the satellite signal in the solar spectrum, 6S: an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675−686. doi: 10.1109/36.581987
    [22] 丁凡. 太湖OLI影像大气校正方法对比与适用性评价[D]. 西安: 西安科技大学, 2018.

    Ding Fan. Comparison and applicability assessment of atmospheric correction methods of OLI images in Taihu Lake[D]. Xi’an: Xi’an University of Science and Technology, 2018.
    [23] Cooley T, Anderson G P, Felde G W, et al. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation[C]//IEEE International Geoscience and Remote Sensing Symposium. New York: IEEE, 2002: 1414−1418.
    [24] Rothman L S, Rinsland C P, Goldman A, et al. The HITRAN molecular spectroscopic database and HAWKS (HITRAN Atmospheric Workstation): 1996 edition[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 1998, 60(5): 665−710. doi: 10.1016/S0022-4073(98)00078-8
    [25] Vanhellemont Q, Ruddick K. Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8[J]. Remote Sensing of Environment, 2015, 161: 89−106. doi: 10.1016/j.rse.2015.02.007
    [26] Dörnhöfer K, Göritz A, Gege P, et al. Water constituents and water depth retrieval from sentinel-2A-A first evaluation in an oligotrophic lake[J]. Remote Sensing, 2016, 8(11): 941. doi: 10.3390/rs8110941
    [27] Bernstein L S, Adler-Golden S M, Sundberg R L, et al. Validation of the Quick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery[C]//Proceedings of SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. Orlando: SPIE, 2005: 668−678.
    [28] Lyzenga D R, Malinas N R, Tanis F J. Multispectral bathymetry using a simple physically based algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2251−2259. doi: 10.1109/TGRS.2006.872909
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  • 收稿日期:  2021-08-14
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