浏览全部资源
扫码关注微信
Published： 07 January 2023 ，
扫 描 看 全 文
张华国，马蕴涵，厉冬玲，曹雯婷，王隽.2023.HY-1C/D卫星CZI数据的岛礁浅海水深遥感无控反演能力评估.遥感学报，27（1）： 116-127
Zhang H G，Ma Y H，Li D L，Cao W T and Wang J. 2023. Retrieval and assessment of island shallow water depth without ground data from the HY-1C/D CZI multispectral imagery. National Remote Sensing Bulletin， 27（1）：116-127
岛礁浅海水深是海洋重要的基础资料。中国南海岛礁远离大陆，水下现场调查效率低、难度大，难以评估水下地形的长期变化，迫切需要大面积、高频次成像的卫星遥感数据。海洋一号C/D（HY-1C/D）卫星双星组网大大提高了覆盖频次，配置的海岸带成像仪CZI（Coastal Zone Imager）可为岛礁水下探测提供快速的业务化遥感数据服务。为充分发掘两颗卫星的岛礁水深反演能力，本文以中国南海永乐环礁为研究区域，以HY-1C/D CZI多光谱遥感影像为数据源，结合半分析模型和对数比值模型开展了不依赖实测数据的水深反演研究，并与GeoEye-1高分辨率卫星多光谱遥感数据反演结果进行对比。结果表明，HY-1C/D CZI多光谱数据在甘泉岛的水深反演结果与实测数据相比，在0—20 m水深范围内的平均绝对误差分别为1.60 m和1.85 m，平均相对误差分别为22.48%和26.23%；HY-1C/D CZI多光谱数据与基于GeoEye-1数据在甘泉岛的水深反演结果相比，平均绝对偏差分别为1.65 m和1.81 m，平均相对偏差分别为22.33%和23.83%。HY-1C/D CZI与GeoEye-1多光谱数据在永乐环礁各剖面变化趋势的交叉对比结果基本一致。具有高频次、宽覆盖特征的HY-1C/D CZI数据在全球岛礁浅海水深反演方面具有广泛的应用潜力。
Shallow water depth of islands and reefs is an important marine element. The islands and reefs in the South China Sea are located far from the mainland
which makes it difficult to assess long-term changes in underwater topography owing to the low efficiency and difficulty in field investigations. Satellite remote sensing imagery capable of large coverage and high frequency is urgently needed. The double satellite network of HY-1C/D greatly improves the coverage frequency. The Coastal Zone Imager (CZI) can provide fast operational remote sensing services for underwater detection of islands and reefs. To fully explore the depth detection capabilities of the two satellites
this study used Yongle Atoll as the research area
with HY-1C/D CZI multispectral remote sensing imagery as the data source. Combined semi-analytical and log-ratio models were used to perform water depth inversion independent of in situ data. The objective is to access the application potential of HY-1C/D CZI imagery for shallow water depth inversion of islands and reefs. This study combined a semi-analytical and logarithmic ratio model ( called L-S model) based on satellite remote sensing imagery of HY-1C/D CZI
which includes four bands from visible to near-infrared. The strong linear relationship between water depth and the relevant spectral parameters of the logarithmic ratio model were used to globally restrict the semi-analytical model. After preprocessing the HY-1C/D CZI domestic multispectral imagery
which included geographic projection
geometric precision correction
calculation of the top of atmosphere reflectance
radiometric correction
sun glint correction
and atmospheric correction
a shallow water depth inversion experiment was carried out in Yongle Atoll
independent of in situ water depth or any other priori knowledge based on the L-S model. The water depth inversion results after tidal height correction using the OSU tidal prediction software in Yongle Atoll were compared with the in situ data and cross-compared with the inversion results based on GeoEye-1 remote sensing imagery. Compared with the in situ water depth
the mean absolute errors of HY-1C/D CZI were 1.60 m and 1.85 m
and the relative errors were 22.48% and 26.23%
respectively. The mean absolute error of the water depth inversion result of GeoEye-1 was 0.78 m and the relative error was 10.86%. Compared with the results of GeoEye-1
the mean average absolute deviation of HY-1C/D CZI were 1.65 m and 1.81 m
and the relative deviations were 22.33% and 23.83%
respectively
which were basically consistent in different sate llite sensors. Although the overall accuracy is lower than that of high-spatial-resolution satellite images
the mean absolute error of the inversion results of HY-1C/D CZI can be controlled within 2.0 m
with a high reference value. This solves the problem of the lack of in situ water depth data during shallow water depth inversion at a large scale. In addition
a cross-comparison of the inversion results between HY-1C and HY-1D showed that this method is robust when applied to different satellite sensors. This indicates that HY-1C/D CZI have the advantages of a short revisiting period and large imaging coverage
which can quickly and repeatedly obtain large-scale optical image data of the ocean and perform shallow water depth mapping of islands and reefs
thereby realizing high-frequency monitoring of underwater terrain changes. HY-1C/D CZI imagery and high-spatial-resolution satellite imagery complement each other and compensate for the shortcomings of field measurements. Therefore
based on the HY-1C/D CZI imagery
the water depth information can be retrieved in the range of 0—20 m stably and accurately
which has a wide range of application potential in the shallow water depth inversion of global islands and reefs.
HY-1C/D海岸带成像仪CZI岛礁浅海水深永乐环礁L-S模型
HY-1C/DCoastal Zone Imager (CZI)island shallow water depthYongle AtollL-S model
Chen B Q, Yang Y M, Xu D W and Huang E H. 2019. A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 1-13 [DOI: 10.1016/j.isprsjprs.2019.02.012http://dx.doi.org/10.1016/j.isprsjprs.2019.02.012]
Dekker A G, Phinn S R, Anstee J, Bissett P, Brando V E, Casey B, Fearns P, Hedley J, Klonowski W, Lee Z P, Lynch M, Lyons M, Mobley C and Roelfsema C. 2011. Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnology and Oceanography: Methods, 9(9): 396-425 [DOI: 10.4319/lom.2011.9.396http://dx.doi.org/10.4319/lom.2011.9.396]
Eugenio F, Marcello J and Martin J. 2015. High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3539-3549 [DOI: 10.1109/TGRS.2014.2377300http://dx.doi.org/10.1109/TGRS.2014.2377300]
Fan C X. 2019. Latest Advances of Global Ocean Tide Models and Their Accuracy Comparisons in Coastal Areas of China. Journal of Geodesy and Geodynamics, 39(5): 6
范长新. 2019. 全球海潮模型最新进展及在中国沿海精度评估. 大地测量与地球动力学, 39(5): 6 [DOI: CNKI:SUN:DKXB.0.2019-05-007http://dx.doi.org/CNKI:SUN:DKXB.0.2019-05-007]
Fan Y G and Liu J X. 2015. Water depth remote sensing retrieval model based on artificial neural network techniques. Hydrographic Surveying and Charting, 35(4): 20-23
樊彦国, 刘金霞. 2015. 基于神经网络技术的遥感水深反演模型研究. 海洋测绘, 35(4): 20-23 [DOI: 10.3969/j.issn.1671-3044.2015.04.005http://dx.doi.org/10.3969/j.issn.1671-3044.2015.04.005]
Huang R Y, Yu K F, Wang Y H, Liu J L and Zhang H Y. 2019. Progress of the study on coral reef remote sensing. Journal of Remote Sensing, 23(6): 1091-1112
黄荣永, 余克服, 王英辉, 刘嘉鎏, 张惠雅. 2019. 珊瑚礁遥感研究进展. 遥感学报, 23(6): 1091-1112 [DOI:10.11834/jrs.20198110http://dx.doi.org/10.11834/jrs.20198110]
Irish J L and Lillycrop W J. 1999. Scanning laser mapping of the coastal zone: the SHOALS system. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2/3): 123-129 [DOI: 10.1016/S0924-2716(99)00003-9http://dx.doi.org/10.1016/S0924-2716(99)00003-9]
Lee Z, Carder K L, Chen R F and Peacock T G. 2001. Properties of the water column and bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Journal of Geophysical Research: Oceans, 106(C6): 11639-11651 [DOI: 10.1029/2000JC000554http://dx.doi.org/10.1029/2000JC000554]
Lee Z, Carder K L, Mobley C D, Steward R G and Patch J S. 1998. Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Applied Optics, 37(27): 6329-6338 [DOI: 10.1364/AO.37.006329http://dx.doi.org/10.1364/AO.37.006329]
Lee Z, Carder K L, Mobley C D, Steward R G and Patch J S. 1999. Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied Optics, 38(18): 3831-3843 [DOI: 10.1364/AO.38.003831http://dx.doi.org/10.1364/AO.38.003831]
Lee Z, Casey B, Arnone R A, Weidemann A D, Parsons R, Montes M J, Gao B C, Goode W, Davis C O and Dye J. 2007. Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform. Journal of Applied Remote Sensing, 1(1): 011502-011516. [DOI: 10.1117/1.2822610http://dx.doi.org/10.1117/1.2822610]
Li L. 2016. Remote sensing bathymetric inversion for the Xisha Islands based on WorldView-2 data: a case study of Zhaoshu Island and South Island. Remote Sensing for Land and Resources, 28(4): 170-175
李丽. 2016. 基于WorldView-2数据的西沙群岛遥感水深反演——以赵述岛和南岛为例. 国土资源遥感, 28(4): 170-175 [DOI: 10.6046/gtzyyg.2016.04.26http://dx.doi.org/10.6046/gtzyyg.2016.04.26]
Liang C, Liu L, Liu J Q, Zou B, Zou Y R and Cui S X. 2020. Extracting mangrove information using MNF transformation based on HY-1C CZI spectral indices reconstruction data. Haiyang Xuebao, 42(4): 104-112
梁超, 刘利, 刘建强, 邹斌, 邹亚荣, 崔松雪. 2020. 基于HY-1C CZI影像光谱指数重构数据MNF变换的红树林提取. 海洋学报, 42(4): 104-112 [DOI: 10.3969/j.issn.0253-4193.2020.04.012http://dx.doi.org/10.3969/j.issn.0253-4193.2020.04.012]
Liu J L, Huang R Y and Yu K F. 2020. Analysis on the geomorphic changes of Huangyan Island based on satellite images over the past 40 years. Quaternary Sciences, 40(3): 775-790
刘嘉鎏, 黄荣永, 余克服. 2020. 黄岩岛环礁地貌近40年变化的遥感分析. 第四纪研究, 40(3): 775-790 [DOI: 10.11928/j.issn.1001-7410.2020.03.15http://dx.doi.org/10.11928/j.issn.1001-7410.2020.03.15]
Liu Y M, Deng R R, Li J, Qin Y, Xiong L H, Chen Q D and Liu X L. 2018. Multispectral bathymetry via linear unmixing of the benthic reflectance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11): 4349-4363 [DOI: 10.1109/JSTARS.2018.2874684http://dx.doi.org/10.1109/JSTARS.2018.2874684]
Liu Y M, Tang D L, Deng R R, Cao B, Chen Q D, Zhang R H, Qin Y and Zhang S Q. 2021. An adaptive blended algorithm approach for deriving bathymetry from multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 801-817[DOI: 10.1109/JSTARS.2020.3034375http://dx.doi.org/10.1109/JSTARS.2020.3034375]
Lu G, Yin F, Chen C, Wang D M and Liang W G. 2017. Water Depth Extraction from Landsat-8 Image in Hongze Lake. Beijing Surveying and Mapping, 000(004): 13-18
卢刚, 尹凡, 陈成, 王冬梅, 梁文广. 2017. 基于Landsat-8遥感影像的洪泽湖水深提取技术研究. 北京测绘, (4): 13-18 [DOI: CNKI:SUN:BJCH.0.2017-04-005http://dx.doi.org/CNKI:SUN:BJCH.0.2017-04-005]
Lyzenga D R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3): 379-383 [DOI: 10.1364/ao.17.000379http://dx.doi.org/10.1364/ao.17.000379]
Ma Y H, Zhang H G, Li X R, Wang J, Cao W T, Li D L, Lou X L and Fan K G. 2021. An exponential algorithm for bottom reflectance retrieval in clear optically shallow waters from multispectral imagery without ground data. Remote Sensing, 13(6): 1169 [DOI: 10.3390/rs13061169http://dx.doi.org/10.3390/rs13061169]
Markham B L and Barker J L. 1987. Thematic Mapper bandpass solar exoatmospheric irradiances. International Journal of Remote Sensing, 8(3): 517-523 [DOI: 10.1080/01431168708948658http://dx.doi.org/10.1080/01431168708948658]
Sheng L, Wang S T, Zhou G W, Bai J and Li Y C. 2015. Research on reefs bathymetry estimation by remote sensing based on nonlinear model. Science of Surveying and Mapping, 40(10): 43-47
盛琳, 王双亭, 周高伟, 白洁, 李英成. 2015. 非线性模型岛礁礁盘遥感水深反演. 测绘科学，40(10): 43-47 [DOI: 10.16251/j.cnki.1009-2307.2015.10.008http://dx.doi.org/10.16251/j.cnki.1009-2307.2015.10.008]
Stumpf R P, Holderied K and Sinclair M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1 part 2): 547-556 [DOI: 10.4319/lo.2003.48.1_part_2.0547http://dx.doi.org/10.4319/lo.2003.48.1_part_2.0547]
Su H B, Liu H X, Wang L, Filippi A M, Heyman W D and Beck R A. 2014. Geographically adaptive inversion model for improving bathymetric retrieval from satellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 465-476 [DOI: 10.1109/TGRS.2013.2241772http://dx.doi.org/10.1109/TGRS.2013.2241772]
Wang J K, Chen Z H, Yu K F, Huang R Y and Wang Y H. 2018. Water depth information extraction with multi-spectral remote sensing in coral reefs region. Remote Sensing Technology and Application, 33(1): 61-67
王纪坤, 陈正华, 余克服, 黄荣永, 王英辉. 2018. 珊瑚礁区多光谱遥感水深反演研究. 遥感技术与应用, 33(1): 61-67 [DOI: 10.11873/j.issn.1004-0323.2018.1.0061http://dx.doi.org/10.11873/j.issn.1004-0323.2018.1.0061]
Wang L M, Liu J, Gao J M, Yao B M, Yang F G and Zou J Q. 2019. Winter wheat early identification based on HY-1C/CZI data. Chinese Agricultural Science Bulletin, 35(33): 151-157
王利民, 刘佳, 高建孟, 姚保民, 杨福刚, 邹金秋. 2019. 基于HY-1C/CZI数据的冬小麦早期识别研究. 中国农学通报, 35(33): 151-157 [DOI: 10.11924/j.issn.1000-6850.casb20190600232http://dx.doi.org/10.11924/j.issn.1000-6850.casb20190600232]
Wang R，Shen Q，Peng H C，Yao Y，Li J S，Wang M X，Shi J R and Xu W T. 2022. Study on the applicability of multi-source high-resolution satellite images for monitoring black and odorous water body. National Remote Sensing Bulletin， 26（1）：179-192 [DOI: 10.11834/jrs.20220479http://dx.doi.org/10.11834/jrs.20220479]
Wang Y H, Chen Y L, Zhou X H, Yang L and Fu Y G. 2018. Research on reef bathymetry using remote sensing based on polynomial regression model. Acta Oceanologica Sinica, 40(3): 121-128
王燕红, 陈义兰, 周兴华, 杨磊, 付延光. 2018. 基于多项式回归模型的岛礁遥感浅海水深反演. 海洋学报，40(3): 121-128 [DOI: 10.3969/j.issn.0253-4193.2018.03.012http://dx.doi.org/10.3969/j.issn.0253-4193.2018.03.012]
Westfeld P, Maas H G, Richter K and Weiß R. 2017. Analysis and correction of ocean wave pattern induced systematic coordinate errors in airborne LiDAR bathymetry. ISPRS Journal of Photogrammetry and Remote Sensing, 128: 314-325 [DOI: 10.1016/j.isprsjprs.2017.04.008http://dx.doi.org/10.1016/j.isprsjprs.2017.04.008]
Xia H Y, Li X R, Zhang H G, Wang J, Lou X L, Fan K G, Shi A Q and Li D L. 2020. A bathymetry mapping approach combining log-ratio and semianalytical models using four-band multispectral imagery without ground data. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2695-2709 [DOI: 10.1109/TGRS.2019.2953381http://dx.doi.org/10.1109/TGRS.2019.2953381]
Zhang J L and Yang X K. 2016. Tidal level forecast and accuracy assessment of coastal tidal stations in China. China Water Transport, 16(12): 234-235
张建良, 杨晓坤. 2016. 中国沿岸验潮站潮位预报及精度评估. 中国水运(下半月), 16(12): 234-235
Zhang L, Teng H Z, Meng C Y, Li J, Xin X H, Ye Q G and Li H B. 2011. Bathymetry using hyperspectral imagery based on semi-analytical model. Hydrographic Surveying and Charting, 31(4): 17-21
张靓, 滕惠忠, 孟婵媛, 李军, 辛宪会, 叶秋果, 李海滨. 2011. 基于半分析模型的高光谱遥感水深探测方法. 海洋测绘, 31(4): 17-21 [DOI: 10.3969/j.issn.1671-3044.2011.04.005http://dx.doi.org/10.3969/j.issn.1671-3044.2011.04.005]
Zhang Z, Zhang J Y, Ma Y, Tian H B and Jiang T. 2019. Retrieval of nearshore bathymetry around Ganquan Island from LiDAR waveform and QuickBird image. Applied Sciences, 9(20): 4375 [DOI: 10.3390/app9204375http://dx.doi.org/10.3390/app9204375]
Zhang Z X and Hao Y L. 2012. Satellite multi-spectral remote sensing inversion in shallow water detection. Navigation of China, 35(1): 13-18
张振兴, 郝燕玲. 2012. 卫星遥感多光谱浅海水深反演法. 中国航海, 35(1): 13-18 [DOI: 10.3969/j.issn.1000-4653.2012.01.004http://dx.doi.org/10.3969/j.issn.1000-4653.2012.01.004]
Zhou M X, Liu Y X, Li M C, Sun C and Zou W. 2015. Geomorphologic information extraction for multi-objective coral islands from remotely sensed imagery: A case study for Yongle Atoll, South China Sea. Geographical Research, 34(4): 677-690
周旻曦, 刘永学, 李满春, 孙超, 邹伟. 2015. 多目标珊瑚岛礁地貌遥感信息提取方法——以西沙永乐环礁为例. 地理研究，34(4): 677-690 [DOI: 10.11821/dlyj201504007http://dx.doi.org/10.11821/dlyj201504007]
相关文章
相关作者
相关机构
京公网安备11010802024621