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Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

LU Tianqi CHEN Shengbo TU Yuan YU Yan CAO Yijing JIANG Deyang

LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. 中国地理科学, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
引用本文: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. 中国地理科学, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
Citation: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

doi: 10.1007/s11769-018-1013-z
基金项目: Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), Plan for Changbai Mountain Scholars of Jilin Province, China
详细信息
    通讯作者:

    CHEN Shengbo.E-mail:chensb@jlu.edu.cn

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

Funds: Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), Plan for Changbai Mountain Scholars of Jilin Province, China
More Information
    Corresponding author: CHEN Shengbo
  • 摘要: Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 (Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0-5 m because of the influence of human activities. The inversion accuracy of 5-10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5-20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.
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    [33] Shu Xiaozhou, Yin Qiu, Kuang Dingbo, 2000. Relationship be-tween algal chlorophyll concentration and spectral reflectance of inland water. Journal of Remote Sensing, 4(1):41-45. (in Chinese)
    [34] Su H B, Liu H X, Heyman W D, 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31(4):281-298. doi: 10.1080/01490410802466652
    [35] Su H B, Liu H X, Wang L et al., 2014. Geographically adaptive inversion model for improving bathymetric retrieval from sat-ellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1):465-476. doi:10.1109/TGRS. 2013.2241772
    [36] Su H B, Liu H X, Wu Q S, 2015. Prediction of water depth from multispectral satellite imagery-the regression Kriging alter-native. IEEE Geoscience and Remote Sensing Letters, 12(12):2511-2515. doi: 10.1109/LGRS.2015.2489678
    [37] Zhao Jianhu, Liu Jingnan, 2008. Multi-beam Sounding Technology and Image Data Processing. Wuhan:Wuhan University Press. (in Chinese)es G, 2016. A modified Lyz-enga's model for multispectral bathymetry using Tikhonov regularization. IEEE Geoscience and Remote Sensing Letters, 13(1):53-57. doi: 10.1109/LGRS.2015.2496401
    [38] Flener C, Lotsari E, Alho P et al., 2012. Comparison of empirical and theoretical remote sensing based bathymetry models in river environments. River Research and Applications, 28(1):118-133. doi: 10.1002/rra.1441
    [39] Gitelson A, 1992. The peak near 700 nm on radiance spectra of algae and water:relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17):3367-3373. doi:10.1080/014311692 08904125
    [40] Gordon H R, 1979. Diffuse reflectance of the ocean:the theory of its augmentation by chlorophyll a fluorescence at 685 nm. Ap-plied Optics, 18(8):1161-1166. doi: 10.1364/AO.18.001161
    [41] Huang R Y, Yu K F, Wang Y H et al., 2017. Bathymetry of the coral reefs of Weizhou Island based on multispectral satellite images. Remote Sensing, 9(7):750. doi: 10.3390/rs9070750
    [42] Jawak S D, Vadlamani S S, Luis A J, 2015. A synoptic review on deriving bathymetry information using remote sensing tech-nologies:models, methods and comparisons. Advances in Re-mote Sensing, 4(2):57480. doi: 10.4236/ars.2015.42013
    [43] Jay S, Guillaume M, Minghelli A et al., 2017. Hyperspectral re-mote sensing of shallow waters:considering environmental noise and bottom intra-class variability for modeling and in-version of water reflectance. Remote Sensing of Environment, 200:352-367. doi: 10.1016/j.rse.2017.08.020
    [44] Johnson S Y, Cochrane G R, Golden N E et al., 2017. The Cali-fornia seafloor and coastal mapping program:providing science and geospatial data for California's State waters. Ocean and Coastal Management, 140:88-104. doi:10.1016/j. oce-coaman.2017.02.004
    [45] Lee Z, Hu C, Arnone R et al., 2012. Impact of sub-pixel variations on ocean color remote sensing products. Optics Express, 20(19):20844-20854. doi: 10.1364/OE.20.020844
    [46] Li Jiabiao, 1999. Principles, Technology and Methods of Multi-beam Survey. Beijing:China Ocean Press. (in Chinese)
    [47] Li J R, Zhang H G, Hou P F et al., 2016. Mapping the bathymetry of shallow coastal water using single-frame fine-resolution op-tical remote sensing imagery. Acta Oceanologica Sinica, 35(1):60-66. doi: 10.1007/s13131-016-0797-x
    [48] Li Qingquan, Lu Yi, Hu Shuibo et al., 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5):1216-1229. (in Chinese)
    [49] Li Xian, Chen Shengbo, Wang Xuhui et al., 2008. Study based on radioactive transfer model of the quantitative remote sensing of water bottom reflectance. Journal of Jilin University (Earth Science Edition), 38(S1):235-237. (in Chinese)
    [50] Lu Tianqi, Chen Shengbo, Guo Tiantian et al., 2016. Offshore bathymetry retrieval from SPOT-6 image. Journal of Marine Sciences, 34(3):51-56. (in Chinese)
    [51] Lyzenga D R, 1978. Passive remote sensing techniques for map-ping water depth and bottom features. Applied Optics, 17(3):379-383. doi: 10.1364/AO.17.000379
    [52] Lyzenga D R, 1979. Shallow-water reflectance modeling with applications to remote sensing of the ocean floor. Proceedings of the 13th International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan:Environmental Research Institute of Michigan, 583-602.
    [53] Lyzenga D R, 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 2(1):71-82. doi: 10.1080/01431168108948342
    [54] Manessa M D M, Kanno A, Sagawa T et al., 2018. Simulation-based investigation of the generality of Lyzenga's multispectral bathymetry formula in Case-1 coral reef water. Estuarine, Coastal and Shelf Science, 200:81-90. doi:10.1016/j.ecss. 2017.10.014
    [55] Mgengel V, Spitzer R J, 1991. Application of remote sensing data to mapping of shallow sea-floor near by Netherlands. Interna-tional Journal of Remote Sensing, 57(5):473-479.
    [56] Odermatt D, Gitelson A, Brando V E et al., 2012. Review of con-stituent retrieval in optically deep and complex waters from satellite imagery. Remote Sensing of Environment, 118:116-126. doi: 10.1016/j.rse.2011.11.013
    [57] Paredes J M, Spero R E, 1983. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Applied Optics, 22(8):1134-1135. doi:10.1364/AO.22. 001134
    [58] Poupardin A, Idier D, de Michele M D et al., 2016. Water depth inversion from a single SPOT-5 dataset. IEEE Transactions on Geoscience and Remote Sensing, 54(4):2329-2342, doi: 10.1109/TGRS.2015.2499379
    [59] Salama M S, Verhoef W, 2015. Two-stream remote sensing model for water quality mapping:2SeaColor. Remote Sensing of Environment, 157:111-122. doi: 10.1016/j.rse.2014.07.022
    [60] Sandidge J C, Holyer R J, 1998. Coastal bathymetry from hyper-spectral observations of water radiance. Remote Sensing of Environment, 65(3):341-352. doi:10.1016/S0034-4257(98) 00043-1
    [61] Shu Xiaozhou, Yin Qiu, Kuang Dingbo, 2000. Relationship be-tween algal chlorophyll concentration and spectral reflectance of inland water. Journal of Remote Sensing, 4(1):41-45. (in Chinese)
    [62] Su H B, Liu H X, Heyman W D, 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31(4):281-298. doi: 10.1080/01490410802466652
    [63] Su H B, Liu H X, Wang L et al., 2014. Geographically adaptive inversion model for improving bathymetric retrieval from sat-ellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(1):465-476. doi:10.1109/TGRS. 2013.2241772
    [64] Su H B, Liu H X, Wu Q S, 2015. Prediction of water depth from multispectral satellite imagery-the regression Kriging alter-native. IEEE Geoscience and Remote Sensing Letters, 12(12):2511-2515. doi: 10.1109/LGRS.2015.2489678
    [65] Zhao Jianhu, Liu Jingnan, 2008. Multi-beam Sounding Technology and Image Data Processing. Wuhan:Wuhan University Press. (in Chinese)
    [66]  
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Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

doi: 10.1007/s11769-018-1013-z
    基金项目:  Under the auspices of the Program for Jilin University Science and Technology Innovative Research Team (No. JLUSTIRT, 2017TD-26), Plan for Changbai Mountain Scholars of Jilin Province, China
    通讯作者: CHEN Shengbo.E-mail:chensb@jlu.edu.cn

摘要: Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 (Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0-5 m because of the influence of human activities. The inversion accuracy of 5-10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5-20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.

English Abstract

LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. 中国地理科学, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
引用本文: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. 中国地理科学, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
Citation: LU Tianqi, CHEN Shengbo, TU Yuan, YU Yan, CAO Yijing, JIANG Deyang. Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data[J]. Chinese Geographical Science, 2019, 20(2): 192-201. doi: 10.1007/s11769-018-1013-z
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