Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5131-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5131-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning
Xudong Zhang
Key Laboratory of Ocean Observation and Forecasting, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Xiaofeng Li
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
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Cited articles
Alford, M. H., Peacock, T., MacKinnon, J. A., Nash, J. D., Buijsman, M. C., Centurioni, L. R., Chao, S. Y., Chang, M. H., Farmer, D. M., Fringer, O. B., Fu, K. H., Gallacher, P. C., Graber, H. C., Helfrich, K. R., Jachec, S. M., Jackson, C. R., Klymak, J. M., Ko, D. S., Jan, S., Johnston, T. M., Legg, S., Lee, I. H., Lien, R. C., Mercier, M. J., Moum, J. N., Musgrave, R., Park, J. H., Pickering, A. I., Pinkel, R., Rainville, L., Ramp, S. R., Rudnick, D. L., Sarkar, S., Scotti, A., Simmons, H. L., St Laurent, L. C., Venayagamoorthy, S. K., Wang, Y. H., Wang, J., Yang, Y. J., Paluszkiewicz, T., and Tang, T. Y.: The formation and fate of internal waves in the South China Sea, Nature, 521, 65–69, https://doi.org/10.1038/nature14399, 2015.
Alpers, W.: Theory of radar imaging of internal waves, Nature, 314, 245–247, 1985.
Bai, X., Liu, Z., Li, X., and Hu, J.: Generation sites of internal solitary waves in the southern Taiwan Strait revealed by MODIS true-colour image observations, Int. J. Remote Sens., 35, 4086–4098, https://doi.org/10.1080/01431161.2014.916453, 2014.
Bai, X., Li, X., Lamb, K. G., and Hu, J.: Internal Solitary Wave Reflection Near Dongsha Atoll, the South China Sea, J. Geophys. Res.-Oceans, 122, 7978–7991, https://doi.org/10.1002/2017jc012880, 2017.
Bao, S., Meng, J., Sun, L., and Liu, Y.: Detection of ocean internal waves based on Faster R-CNN in SAR images, J. Oceanol. Limnol., 38, 55–63, https://doi.org/10.1007/s00343-019-9028-6, 2019.
Cai, S., Xie, J., and He, J.: An Overview of Internal Solitary Waves in the South China Sea, Surv. Geophys., 33, 927–943, https://doi.org/10.1007/s10712-012-9176-0, 2012.
de Macedo, C. R., Koch-Larrouy, A., da Silva, J. C. B., Magalhães, J. M., Lentini, C. A. D., Tran, T. K., Rosa, M. C. B., and Vantrepotte, V.: Spatial and temporal variability in mode-1 and mode-2 internal solitary waves from MODIS-Terra sun glint off the Amazon shelf, Ocean Sci., 19, 1357–1374, https://doi.org/10.5194/os-19-1357-2023, 2023.
Dong, D., Yang, X. F., Li, X. F., and Li, Z. W.: SAR Observation of Eddy-Induced Mode-2 Internal Solitary Waves in the South China Sea, IEEE T. Geosci. Remote, 54, 6674–6686, https://doi.org/10.1109/Tgrs.2016.2587752, 2016.
Furtney, S., Romeiser, R., and Graber, H. C.: Automated retrieval of internal wave phase speed and direction from pairs of SAR images with different look directions, Remote Sens. Environ., 305, 114084, https://doi.org/10.1016/j.rse.2024.114084, 2024.
Gong, Y., Chen, X., Xu, J., Xie, J., Chen, Z., He, Y., and Cai, S.: An internal solitary wave forecasting model in the northern South China Sea (ISWFM-NSCS), Geosci. Model Dev., 16, 2851–2871, https://doi.org/10.5194/gmd-16-2851-2023, 2023.
Guo, C. and Chen, X.: A review of internal solitary wave dynamics in the northern South China Sea, Prog. Oceanogr., 121, 7–23, https://doi.org/10.1016/j.pocean.2013.04.002, 2014.
Haury, L. R., Briscoe, M. G., and Orr, M. H.: Tidally generated internal wave packets in Massachusetts Bay, Nature, 278, 312–317, https://doi.org/10.1038/278312a0, 1979.
Hu, B. L., Meng, J. M., Sun, L. N., and Zhang, H.: A Study on Brightness Reversal of Internal Waves in the Celebes Sea Using Himawari-8 Images, Remote Sens., 13, 3831, https://doi.org/10.3390/Rs13193831, 2021.
Jia, T., Liang, J. J., Li, X. M., and Sha, J.: SAR Observation and Numerical Simulation of Internal Solitary Wave Refraction and Reconnection Behind the Dongsha Atoll, J. Geophys. Res.-Oceans, 123, 74–89, https://doi.org/10.1002/2017jc013389, 2018.
Jia, Y., Tian, Z., Shi, X., Liu, J. P., Chen, J., Liu, X., Ye, R., Ren, Z., and Tian, J.: Deep-sea sediment resuspension by internal solitary waves in the northern South China Sea, Sci. Rep., 9, 12137, https://doi.org/10.1038/s41598-019-47886-y, 2019.
Kurekin, A. A., Land, P. E., and Miller, P. I.: Internal Waves at the UK Continental Shelf: Automatic Mapping Using the ENVISAT ASAR Sensor, Remote Sens., 12, 2476, https://doi.org/10.3390/rs12152476, 2020.
Li, Q., Wang, B., Chen, X., Chen, X., and Park, J. H.: Variability of nonlinear internal waves in the South China Sea affected by the Kuroshio and mesoscale eddies, J. Geophys. Res.-Oceans, 121, 2098–2118, https://doi.org/10.1002/2015jc011134, 2016.
Li, X., Zhao, Z., and Pichel, W. G.: Internal solitary waves in the northwestern South China Sea inferred from satellite images, Geophys. Res. Lett., 35, L13605, https://doi.org/10.1029/2008gl034272, 2008.
Li, X., Jackson, C. R., and Pichel, W. G.: Internal solitary wave refraction at Dongsha Atoll, South China Sea, Geophys. Res. Lett., 40, 3128–3132, https://doi.org/10.1002/grl.50614, 2013.
Li, X., Zhou, Y., and Wang, F.: Advanced Information Mining from Ocean Remote Sensing Imagery with Deep Learning, J. Remote Sens., 2022, 1–4, https://doi.org/10.34133/2022/9849645, 2022.
Li, X. F., Liu, B., Zheng, G., Ren, Y. B., Zhang, S. S., Liu, Y. J., Gao, L., Liu, Y. H., Zhang, B., and Wang, F.: Deep-learning-based information mining from ocean remote-sensing imagery, Natl. Sci. Rev., 7, 1584–1605, https://doi.org/10.1093/nsr/nwaa047, 2020.
Liang, J., Li, X.-M., Sha, J., Jia, T., and Ren, Y.: The Lifecycle of Nonlinear Internal Waves in the Northwestern South China Sea, J. Phys. Oceanogr., 49, 2133–2145, https://doi.org/10.1175/jpo-d-18-0231.1, 2019.
Liu, A. K. and Hsu, M. K.: Internal wave study in the South China Sea using Synthetic Aperture Radar (SAR), Int. J. Remote Sens., 25, 1261–1264, https://doi.org/10.1080/01431160310001592148, 2004.
Liu, B., Yang, H., Zhao, Z., and Li, X.: Internal solitary wave propagation observed by tandem satellites, Geophys. Res. Lett., 41, 2077–2085, https://doi.org/10.1002/2014GL059281, 2014.
Liu, B., Li, X., and Zheng, G.: Coastal inundation mapping from bitemporal and dual-polarization SAR imagery based on deep convolutional neural networks, J. Geophys. Res.-Oceans, 124, 9101–9113, https://doi.org/10.1029/2019jc015577, 2019.
Liu, T. and Abernathey, R.: A global Lagrangian eddy dataset based on satellite altimetry, Earth Syst. Sci. Data, 15, 1765–1778, https://doi.org/10.5194/essd-15-1765-2023, 2023.
Liu, T. Y., Xu, J. X., He, Y. H., Lü, H. B., Yao, Y., and Cai, S. Q.: Numerical simulation of the Kuroshio intrusion into the South China Sea by a passive tracer, Acta Oceanol. Sin., 35, 1–12, https://doi.org/10.1007/s13131-016-0930-x, 2016.
Liu, T. Y., He, Y. H., Zhai, X. M., and Liu, X. H.: Diagnostics of Coherent Eddy Transport in the South China Sea Based on Satellite Observations, Remote Sens., 14, 14071690, https://doi.org/10.3390/rs14071690, 2022.
Ma, Y. T., Meng, J. M., Sun, L. N., and Ren, P.: Oceanic Internal Wave Signature Extraction in the Sulu Sea by a Pixel Attention U-Net: PAU-Net, IEEE Geosci. Remote Sens. Lett., 20, 4000905, https://doi.org/10.1109/Lgrs.2022.3230086, 2023.
Magalhaes, J. M., da Silva, J. C. B., and Buijsman, M. C.: Long lived second mode internal solitary waves in the Andaman Sea, Sci. Rep., 10, 10234, https://doi.org/10.1038/s41598-020-66335-9, 2020.
Magalhaes, J. M., da Silva, J. C. B., Nolasco, R., Dubert, J., and Oliveira, P. B.: Short timescale variability in large-amplitude internal waves on the western Portuguese shelf, Cont. Shelf Res., 246, 104812, https://doi.org/10.1016/j.csr.2022.104812, 2022.
Pan, J., Jay, D. A., and Orton, P. M.: Analyses of internal solitary waves generated at the Columbia River plume front using SAR imagery, J. Geophys. Res.-Oceans, 112, C07014, https://doi.org/10.1029/2006jc003688, 2007.
Ramp, S. R., Yang, Y. J., Chiu, C.-S., Reeder, D. B., and Bahr, F. L.: Observations of shoaling internal wave transformation over a gentle slope in the South China Sea, Nonlin. Processes Geophys., 29, 279–299, https://doi.org/10.5194/npg-29-279-2022, 2022a.
Ramp, S. R., Yang, Y. J., Jan, S., Chang, M. H., Davis, K. A., Sinnett, G., Bahr, F. L., Reeder, D. B., Ko, D. S., and Pawlak, G.: Solitary waves impinging on an Isolated tropical reef: arrival patterns and wave transformation under shoaling, J. Geophys. Res.-Oceans, 127, e2021JC017781, https://doi.org/10.1029/2021jc017781, 2022b.
Sun, L., Zhang, J., and Meng, J.: Study on the propagation velocity of internal solitary waves in the Andaman Sea using Terra/Aqua-MODIS remote sensing images, J. Oceanol. Limnol., 39, 2195–2208, https://doi.org/10.1007/s00343-020-0280-6, 2021.
Tao, M., Xu, C., Guo, L., Wang, X., and Xu, Y.: An Internal Waves Data Set From Sentinel-1 Synthetic Aperture Radar Imagery and Preliminary Detection, Earth Space Sci., 9, e2022EA002528, https://doi.org/10.1029/2022EA002528, 2022.
Wang, H. and Li, X.: DeepBlue: Advanced convolutional neural network applications for ocean remote sensing, IEEE Geosci. Remote Sen. Mag., 12, 138–161, https://doi.org/10.1109/MGRS.2023.3343623, 2023.
Xie, J., He, Y., Lü, H., Chen, Z., Xu, J., and Cai, S.: Distortion and broadening of internal solitary wavefront in the northeastern South China Sea deep basin, Geophys. Res. Lett., 43, 7617–7624, https://doi.org/10.1002/2016gl070093, 2016.
Xu, J., He, Y., Chen, Z., Zhan, H., Wu, Y., Xie, J., Shang, X., Ning, D., Fang, W., and Cai, S.: Observations of different effects of an anti-cyclonic eddy on internal solitary waves in the South China Sea, Prog. Oceanogr., 188, 102422, https://doi.org/10.1016/j.pocean.2020.102422, 2020.
Zhang, M., Wang, J., Chen, X., Mei, Y., and Zhang, X.: An experimental study on the characteristic pattern of internal solitary waves in optical remote-sensing images, Int. J. Remote Sens., 40, 7017–7032, https://doi.org/10.1080/01431161.2019.1597308, 2019.
Zhang, S., Li, X., and Zhang, X.: Internal Wave Signature Extraction from SAR and Optical Satellite Imagery Based on Deep Learning, IEEE T. Geosci. Remote, 61, 1-16, https://doi.org/10.1109/TGRS.2023.3258189, 2023.
Zhang, X. and Li, X.: Deep Learning-Derived Long-Term Dataset of Internal Waves in the Northern South China Sea from Satellite Imagery, Marine Science Data Center of the Chinese Academy of Sciences [data set], https://doi.org/10.12157/IOCAS.20240409.001, 2024.
Zhang, X., Wang, H., Wang, S., Liu, Y., Yu, W., Wang, J., Xu, Q., and Li, X.: Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model, Remote Sens. Environ., 272, 112940, https://doi.org/10.1016/j.rse.2022.112940, 2022.
Zhao, W., Huang, X., and Tian, J.: A new method to estimate phase speed and vertical velocity of internal solitary waves in the South China Sea, J. Oceanogr., 68, 761–769, https://doi.org/10.1007/s10872-012-0132-x, 2012.
Zhao, Z., Klemas, V., Zheng, Q., Li, X., and Yan, X.: Estimating parameters of a two-layer stratified ocean from polarity conversion of internal solitary waves observed in satellite SAR images, Remote Sens. Environ., 92, 276–287, https://doi.org/10.1016/j.rse.2004.05.014, 2004.
Zhao, Z., Liu, B., and Li, X.: Internal solitary waves in the China seas observed using satellite remote-sensing techniques: a review and perspectives, Int. J. Remote Sens., 35, 3926–3946, https://doi.org/10.1080/01431161.2014.916442, 2014.
Zheng, Q., Yuan, Y., Klemas, V., and Yan, X.-H.: Theoretical expression for an ocean internal soliton synthetic aperture radar image and determination of the soliton characteristic half width, J. Geophys. Res.-Oceans, 106, 31415–31423, https://doi.org/10.1029/2000jc000726, 2001.
Zheng, Y. G., Zhang, H. S., and Wang, Y. Q.: Stripe detection and recognition of oceanic internal waves from synthetic aperture radar based on support vector machine and feature fusion, Int. J. Remote Sens., 42, 6710–6728, https://doi.org/10.1080/01431161.2021.1943040, 2021.
Short summary
Internal wave (IW) is an important ocean process and is frequently observed in the South China Sea (SCS). This study presents a detailed IW dataset for the northern SCS spanning from 2000 to 2022, with a spatial resolution of 250 m, comprising 3085 IW MODIS images. This dataset can enhance understanding of IW dynamics and serve as a valuable resource for studying ocean dynamics, validating numerical models, and advancing AI-driven model building, fostering further exploration into IW phenomena.
Internal wave (IW) is an important ocean process and is frequently observed in the South China...
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