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基于GeoEye-1和WorldView-2遥感数据的浅海水深反演比较研究

陆天启 吴志芳 任潇洒 姚慧敏 邵长高

陆天启,吴志芳,任潇洒,等. 基于GeoEye-1和WorldView-2遥感数据的浅海水深反演比较研究[J]. 海洋学报,2022,44(4):134–142 doi: 10.12284/hyxb2022082
引用本文: 陆天启,吴志芳,任潇洒,等. 基于GeoEye-1和WorldView-2遥感数据的浅海水深反演比较研究[J]. 海洋学报,2022,44(4):134–142 doi: 10.12284/hyxb2022082
Lu Tianqi,Wu Zhifang,Ren Xiaosa, et al. Comparative study on shallow water depth inversion based on GeoEye-1 and WorldView-2 remote sensing data[J]. Haiyang Xuebao,2022, 44(4):134–142 doi: 10.12284/hyxb2022082
Citation: Lu Tianqi,Wu Zhifang,Ren Xiaosa, et al. Comparative study on shallow water depth inversion based on GeoEye-1 and WorldView-2 remote sensing data[J]. Haiyang Xuebao,2022, 44(4):134–142 doi: 10.12284/hyxb2022082

基于GeoEye-1和WorldView-2遥感数据的浅海水深反演比较研究

doi: 10.12284/hyxb2022082
基金项目: 中国地质调查局地调项目(DD20211362,DD20191037);三亚崖州湾科技管理局2020年度科技计划(SKJC-2020-01-008)。
详细信息
    作者简介:

    陆天启(1992—),男,江苏省新沂市人,博士,主要从事遥感定量反演研究。E-mail:lutq2014@126.com

    通讯作者:

    吴志芳,女,山东省日照市人,工程师,主要从事遥感与地理信息系统研究。E-mail:764362840@qq.com

  • 中图分类号: P237

Comparative study on shallow water depth inversion based on GeoEye-1 and WorldView-2 remote sensing data

  • 摘要: 浅海区水深的精确反演对于海洋空间管理和生态环境保护至关重要。选取南海西沙群岛的羚羊礁海域为研究区,基于GeoEye-1和WorldView-2高分辨率多光谱遥感数据和实测水深数据,分别建立了单波段模型、多波段模型和波段比值模型。结果显示,由绿波段参与建立的水深反演模型相关性普遍较高,同时利用4个波段组合建立的多波段模型精度最高,相关系数分别达到了0.870和0.853。基于该模型的反演结果对GeoEye-1和WorldView-2遥感数据在不同水深范围内的反演精度进行比较,结果表明,两种数据在不同水深范围内的反演误差变化趋势一致,平均相对误差最大值均出现在0~5 m,而最小值均出现在20~25 m。总体而言,WorldView-2影像反演水深的精度高于GeoEye-1影像的反演精度。研究对于热带浅海区的水深反演工作具有一定的参考意义。
  • 图  1  研究区及水深实测点

    Fig.  1  Study area and measured points

    图  2  大气校正前后光谱曲线

    Fig.  2  Spectral curves before and after atmospheric correction

    图  3  不同波段(波段组合)与水深的相关系数

    Fig.  3  Different bands (band combinations) and their correlation coefficients with water depth

    图  4  4个波段建立的多波段模型水深值与实测水深值散点图

    Fig.  4  Scatter plots of multi-band model water depth value and measured water depth value established by four bands

    图  5  水深反演结果

    Fig.  5  Water depth inversion results

    图  6  水深反演误差变化趋势

    Fig.  6  Variation trend of depth inversion error

    表  1  遥感卫星数据参数

    Tab.  1  Remote sensing satellite data parameters

    参数GeoEye-1WorldView-2
    获取时间2017年2月18日2019年7月15日
    波长范围/nm波段1(蓝):450~510
    波段2(绿):510~580
    波段3(红):655~690
    波段4(近红外):780~920
    波段1(海岸):400~450
    波段2(蓝):450~510
    波段3(绿):510~580
    波段4(黄):585~625
    波段5(红):630~690
    波段6(红边):705~745
    波段7(近红外):770~895
    波段8(近红外):860~1 040
    空间分辨率/m1.651.80
    下载: 导出CSV

    表  2  水深反演模型及其决定系数

    Tab.  2  Water depth inversion model and its determination coefficients

    模型GeoEye-1 WorldView-2
    拟合公式决定系数拟合公式决定系数
    单波段
    模型
    y=−15.618ln(Xb)+121.290 0.563 y=−19.630ln(Xb)+153.310 0.537
    y=−11.989ln(Xg)+90.712 0.738 y=−14.280ln(Xg)+109.510 0.680
    y=−9.770ln(Xr)+64.625 0.091 y=0.427ln(Xr)+19.500 0.001
    y=8.866ln(Xnir)−12.766 0.033 y=1.120ln(Xnir)+17.337 0.007
    多波段
    模型
    y=4.179ln(Xb)–14.544ln(Xg)+78.751 0.745 y=6.573ln(Xb)–18.234ln(Xg)+89.696 0.689
    y=−14.985ln(Xb)–5.847ln(Xr)+143.192 0.595 y=−21.155ln(Xb)–4.477ln(Xr)+181.884 0.573
    y=−15.385ln(Xb)+4.275ln(Xnir)+103.399 0.571 y=20.910ln(Xb)+3.227ln(Xnir)+150.687 0.592
    y=−11.957ln(Xg)–0.212ln(Xr)+91.471 0.739 y=15.027ln(Xg)–4.056ln(Xr)+130.695 0.712
    y=−11.854ln(Xg)+4.770ln(Xnir)+71.632 0.748 y=−14.617ln(Xg) +2.369ln(Xnir)+103.316 0.711
    y=−10.565ln(Xr)+10.653ln(Xnir)+27.285 0.138 y=1.674ln(Xr)+1.607ln(Xnir)+8.803 0.011
    y=4.745ln(Xb)–15.041ln(Xg)+1.010ln(Xr)+73.513 0.746 y=4.415ln(Xb)–17.636ln(Xg)–3.811ln(Xr)+116.107 0.716
    y=4.670ln(Xb)–14.698ln(Xg)+5.180ln(Xnir)+56.621 0.756 y=3.433ln(Xb)–16.656ln(Xg)+2.198ln(Xnir)+93.418 0.713
    y=−14.626ln(Xb)–6.357ln(Xr)+5.577ln(Xnir)+121.762 0.607 y=−21.520ln(Xb)–2.591ln(Xr)+2.539ln(Xnir)+167.786 0.603
    y =−11.736ln(Xg)–0.757ln(Xr)+4.938ln(Xnir) +73.666 0.748 y=−15.025ln(Xg)–2.823ln(Xr)+1.587ln(Xnir)+120.108 0.723
    y=4.942ln(Xb)–14.941ln(Xg)+0.499ln(Xr)+5.093ln(Xnir)+54.407 0.757 y=2.926ln(Xb)–16.754ln(Xg)–2.761ln(Xr)+1.458ln(Xnir)+111.300 0.727
    波段比
    值模型
    ${y=21.978{\rm{ln}} \left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{\rm{g}}}}}} \right)+7.811 }$ 0.561 ${y=28.096 \ln \left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{\rm{g}}}}}} \right) +5.834}$ 0.569
    ${y=-9.668\ln \left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{\rm{r}}}}}} \right) +40.248}$ 0.258 $ {y=-6.429\ln\left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{\rm{r}}}}}} \right) +38.193}$ 0.182
    ${y=-13.35\ln \left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{{\rm{nir}}}}}} } \right)+55.538}$ 0.531 ${y=-5.798\ln \left( {\dfrac{{{X_{\rm{b}}}}}{{{X_{{\rm{nir}}}}}} } \right)+40.067}$ 0.195
    ${y=-11.45\ln \left( {\dfrac{{{X_{\rm{g}}}}}{{{X_{\rm{r}}}}} } \right)+36.747}$ 0.598 ${y=-7.341\ln \left( {\dfrac{{{X_{\rm{g}}}}}{{{X_{\rm{r}}}}} } \right)+36.571}$ 0.356
    ${y=-11.17\ln\left( \dfrac{ { {X_{\rm{g} } } }}{ {X_{ {\rm{nir} } } }} \right)+43.112}$ 0.730 ${y=-6.814\ln\left( \dfrac{ { {X_{\rm{g} } } }}{ {X_{ {\rm{nir} } } } } \right)+39.628}$ 0.367
    ${y=-10.59\ln\left( \dfrac{ { {X_{\rm{r} } } }}{ {X_{ {\rm{nir} } } }} \right)+27.64}$ 0.138 ${y=-0.494\ln\left( \dfrac{ { {X_{\rm{r} } } }}{ {X_{ {\rm{nir} } } }} \right)+21.547}$ 0.003
      注:XbXgXrXnir分别为蓝波段、绿波段、红波段、近红外波段的反射率。
    下载: 导出CSV

    表  3  不同水深范围的反演误差

    Tab.  3  Inversion error of different depth ranges

    水深范围/m平均相对误差/%均方根误差/m
    GeoEye-1WorldView-2GeoEye-1WorldView-2
    0~523.4319.560.870.83
    5~1022.4216.602.251.88
    10~1516.1913.502.532.15
    15~2013.1910.712.702.43
    20~258.857.732.392.14
    25~3013.909.473.792.94
    0~3012.5810.402.702.53
    下载: 导出CSV
  • [1] Li Jiwei, Knapp D E, Schill S R, et al. Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites[J]. Remote Sensing of Environment, 2019, 232: 111302. doi: 10.1016/j.rse.2019.111302
    [2] Hsu H J, Huang C Y, Jasinski M, et al. A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: a case study in the South China Sea[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 178: 1−19. doi: 10.1016/j.isprsjprs.2021.05.012
    [3] Gao J. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations[J]. Progress in Physical Geography, 2009, 33(1): 103−116. doi: 10.1177/0309133309105657
    [4] Jawak S D, Vadlamani S S, Luis A J. A synoptic review on deriving bathymetry information using remote sensing technologies: models, methods and comparisons[J]. Advances in Remote Sensing, 2015, 4(2): 147−162. doi: 10.4236/ars.2015.42013
    [5] Su Haibin, Liu Hongxing, Wu Qiusheng. Prediction of water depth from multispectral satellite imagery—the regression Kriging alternative[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2511−2515. doi: 10.1109/LGRS.2015.2489678
    [6] Civco D L, Kennard W C. Satellite remote bathymetry: a new mechanism for modeling[J]. Photogrammetric Engineering and Remote Sensing, 1992, 58(5): 545−549.
    [7] Paredes J M, Spero R E. Water depth mapping from passive remote sensing data under a generalized ratio assumption[J]. Applied Optics, 1983, 22(8): 1134−1135. doi: 10.1364/AO.22.001134
    [8] Stumpf R P, Holderied K, Sinclair M. Determination of water depth with high-resolution satellite imagery over variable bottom types[J]. Limnology and Oceanography, 2003, 48(1): 547−556.
    [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] Cheng Jie, Ma Yi, Zhang Jingyu. Water-depth-zoning inversion based on the relationship between two-band radiance data and the depth-invariant index[J]. Regional Studies in Marine Science, 2021, 44: 101790. doi: 10.1016/j.rsma.2021.101790
    [11] Liceaga-Correa M A, Euan-Avila J I. Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data[J]. International Journal of Remote Sensing, 2002, 23(1): 3−14. doi: 10.1080/01431160010008573
    [12] Lu Tianqi, Chen Shengbo, Tu Yuan, et al. Comparative study on coastal depth inversion based on multi-source remote sensing data[J]. Chinese Geographical Science, 2019, 29(2): 192−201. doi: 10.1007/s11769-018-1013-z
    [13] Liu Yongming, Tang Danling, Deng Ruru, et al. An adaptive blended algorithm approach for deriving bathymetry from multispectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14: 801−817.
    [14] 聂荣娟. 基于Worldview3数据的浅海水深反演研究[J]. 北京测绘, 2019, 33(9): 1081−1086.

    Nie Rongjuan. Deep seawater inversion based on Worldview3 data[J]. Beijing Surveying and Mapping, 2019, 33(9): 1081−1086.
    [15] 王燕红, 陈义兰, 周兴华, 等. 基于多项式回归模型的岛礁遥感浅海水深反演[J]. 海洋学报, 2018, 40(3): 121−128.

    Wang Yanhong, Chen Yilan, Zhou Xinghua, et al. Research on reef bathymetry using remote sensing based on polynomial regression model[J]. Haiyang Xuebao, 2018, 40(3): 121−128.
    [16] 韩中含, 徐白山, 杨成林, 等. 基于Planet多光谱影像的南海岛礁水深反演研究[J]. 测绘与空间地理信息, 2020, 43(12): 139−142,146. doi: 10.3969/j.issn.1672-5867.2020.12.039

    Han Zhonghan, Xu Baishan, Yang Chenglin, et al. Research on reef depth retrieval of South China Sea island based on Planet multispectral image[J]. Geomatics & Spatial Information Technology, 2020, 43(12): 139−142,146. doi: 10.3969/j.issn.1672-5867.2020.12.039
    [17] Casal G, Hedley J D, Monteys X, et al. Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data[J]. Estuarine, Coastal and Shelf Science, 2020, 241: 106814. doi: 10.1016/j.ecss.2020.106814
    [18] 陈本清, 杨燕明, 罗凯. 基于高分一号卫星多光谱数据的岛礁周边浅海水深遥感反演[J]. 热带海洋学报, 2017, 36(2): 70−78.

    Chen Benqing, Yang Yanming, Luo Kai. Retrieval of island shallow water depth from the GaoFen-1 multi-spectral imagery[J]. Journal of Tropical Oceanography, 2017, 36(2): 70−78.
    [19] Xia Haoyang, Li Xiaorun, Zhang Huaguo, et al. A bathymetry mapping approach combining log-ratio and semianalytical models using four-band multispectral imagery without ground data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2695−2709. doi: 10.1109/TGRS.2019.2953381
    [20] 冯英辞, 詹文欢, 姚衍桃, 等. 西沙群岛礁区的地质构造及其活动性分析[J]. 热带海洋学报, 2015, 34(3): 48−53. doi: 10.3969/j.issn.1009-5470.2015.03.006

    Feng Yingci, Zhan Wenhuan, Yao Yantao, et al. Analysis of tectonic movement and activity in the organic reef region around the Xisha Islands[J]. Journal of Tropical Oceanography, 2015, 34(3): 48−53. doi: 10.3969/j.issn.1009-5470.2015.03.006
    [21] 陈玲, 陈理, 李伟, 等. 基于FLAASH模型的Worldview3大气校正[J]. 国土资源遥感, 2019, 31(4): 26−31.

    Chen Ling, Chen Li, Li Wei, et al. Atmospheric correction of Worldview3 image based on FLAASH model[J]. Remote Sensing for Land & Resources, 2019, 31(4): 26−31.
    [22] 陆天启, 陈圣波, 郭甜甜, 等. 基于SPOT-6遥感影像的近海水深反演[J]. 海洋学研究, 2016, 34(3): 51−56.

    Lu Tianqi, Chen Shengbo, Guo Tiantian, et al. Offshore bathymetry retrieval from SPOT-6 image[J]. Journal of Marine Sciences, 2016, 34(3): 51−56.
    [23] 赵洪臣. 基于WorldView-2的多级决策的光学遥感水深反演方法研究[D]. 南京: 南京大学, 2017.

    Zhao Hongchen. Water depth inversion method of optical remote sensing using multilevel decision-making scheme based on WorldView-2[D]. Nanjing: Nanjing University, 2017.
    [24] 潘昀, 程永舟, 李青峰, 等. 破碎波作用下沙坝附近悬浮泥沙浓度试验研究[J]. 人民长江, 2013, 44(21): 71−75. doi: 10.3969/j.issn.1001-4179.2013.21.019

    Pan Yun, Cheng Yongzhou, Li Qingfeng, et al. Experimental study on suspended sediment concentration near sand bars under action of breaking waves[J]. Yangtze River, 2013, 44(21): 71−75. doi: 10.3969/j.issn.1001-4179.2013.21.019
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  • 收稿日期:  2021-07-16
  • 修回日期:  2021-09-23
  • 刊出日期:  2022-04-14

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