Früher|Nächster

Retrieval and assessment of island shallow water depth without ground data from the HY-1C/D CZI multispectral imagery

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

    School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China

  • Email：zhanghg@sio.org.cn
  • Introduction：张华国，研究方向为近海与海岸带遥感。E-mail： zhanghg@sio.org.cn
ZHANG Huaguo12,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

MA Yunhan1,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

LI Dongling1,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

Cao Wenting1,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

Wang Juan1

Resümee

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.

Schlüsselwort

HY-1C/D;Coastal Zone Imager (CZI);island shallow water depth;Yongle Atoll;L-S model

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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.
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    Lyzenga D R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 17(3): 379-383
  19. 19.
    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
  20. 20.
    Markham B L and Barker J L. 1987. Thematic Mapper bandpass solar exoatmospheric irradiances. International Journal of Remote Sensing, 8(3): 517-523
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website