International Journal of Marine Science, 2025, Vol.15, No.1, 45-52 http://www.aquapublisher.com/index.php/ijms 51 Chen K.H., Zhang J.L., Zheng Y., and Xie X.T., 2024, A study on global oceanic chlorophyll-a concentration inversion model for MODIS using machine learning algorithms, IEEE Access, 12: 128843-128859. https://doi.org/10.1109/ACCESS.2024.3456481 Chirayath V., and Li A., 2019, Next-generation optical sensing technologies for exploring ocean worlds-NASA FluidCam MiDAR and NeMO-Net, Frontiers in Marine Science, 6: 521. https://doi.org/10.3389/fmars.2019.00521 D’Alimonte D., Kajiyama T., and Saptawijaya A., 2016, Ocean color remote sensing of atypical marine optical cases, IEEE Transactions on Geoscience and Remote Sensing, 54: 6574-6586. https://doi.org/10.1109/TGRS.2016.2587106 Feng X., Tian T., Zhou M.Z., Sun H.X., Li D.Z., Tian F., and Lin R.B., 2024, Sound speed inversion based on multi-source ocean remote sensing observations and machine learning, Remote Sensing, 16(5): 814. https://doi.org/10.3390/rs16050814 Goodman J.A., Lay M., Ramirez L., Ustin S.L., and Haverkamp P., 2020, Confidence levels sensitivity and the role of bathymetry in coral reef remote sensing, Remote Sensing, 12(3): 496. https://doi.org/10.3390/rs12030496 Hasan K., Ahmad S., Liaf A., Karimi M., Ahmed T., Shawon M., and Mekhilef S., 2024, Oceanic challenges to technological solutions: a review of autonomous underwater vehicle path technologies in biomimicry control navigation and sensing, IEEE Access, 12: 46202-46231. https://doi.org/10.1109/ACCESS.2024.3380458 He X.Q., Pan T.F., Bai Y., Shanmugam P., Wang D.F., Li T., and Gong F., 2024, Intelligent atmospheric correction algorithm for polarization ocean color satellite measurements over the open ocean, IEEE Transactions on Geoscience and Remote Sensing, 62: 1-22. https://doi.org/10.1109/TGRS.2023.3348159 Ilori C.O., Pahlevan N., and Knudby A., 2019, Analyzing performances of different atmospheric correction techniques for landsat 8: application for coastal remote sensing, Remote Sensing, 11(4): 469. https://doi.org/10.3390/rs11040469 Jiang X.L., and Wang W.F., 2024, Climate change and aquatic ecosystem health: impacts adaptation strategies and future challenges, International Journal of Aquaculture, 14(4): 221-231. https://doi.org/10.5376/ija.2024.14.0022 Jin C., and Pan Y.L., 2024, Application of algae biomarkers in water quality monitoring, International Journal of Aquaculture, 14(1): 29-36. https://doi.org/10.5376/ija.2024.14.0004 Kolluru S., Tiwari S.P., and Gedam S.S., 2021, Hybrid inversion algorithms for retrieval of absorption subcomponents from ocean colour remote sensing reflectance, Remote Sensing, 13(9): 1726. https://doi.org/10.3390/rs13091726 Li H., He X., Ding J., Bai Y., Wang D., Gong F., and Li T., 2022, The inversion of HY-1C-COCTS ocean color remote sensing products from high-latitude seas, Remote Sensing, 14(22): 5722. https://doi.org/10.3390/rs14225722 Li H., Qu K., and Zhou J., 2021, Reconstructing sound speed profile from remote sensing data: nonlinear inversion based on self-organizing map, IEEE Access, 9: 109754-109762. https://doi.org/10.1109/ACCESS.2021.3102608 Li X., Liu B., Zheng G., Ren Y., Zhang S., Liu Y., Gao L., Liu Y., Zhang B., and Wang F., 2020, Deep-learning-based information mining from ocean remote-sensing imagery, National Science Review, 7: 1584-1605. https://doi.org/10.1093/nsr/nwaa047 Liu H., Zhou Q., Li Q., Hu S., Shi T., and Wu G., 2019, Determining switching threshold for NIR-SWIR combined atmospheric correction algorithm of ocean color remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 153: 59-73. https://doi.org/10.1016/J.ISPRSJPRS.2019.04.013 Lu H., Li Y., and Liu B., 2023, Energy-based unmixing method for low background concentration oil spills at sea, Remote Sensing, 15(8): 2079. https://doi.org/10.3390/rs15082079 Petit T., Bajjouk T., Mouquet P., Rochette S., Vozel B., and Delacourt C., 2017, Hyperspectral remote sensing of coral reefs by semi-analytical model inversion - comparison of different inversion setups, Remote Sensing of Environment, 190: 348-365. https://doi.org/10.1016/J.RSE.2017.01.004 Sahoo A., Dwivedy S.K., and Robi P.S., 2019, Advancements in the field of autonomous underwater vehicle, Ocean Engineering, 181: 145-160. https://doi.org/10.1016/J.OCEANENG.2019.04.011 Seydi S.T., Hasanlou M., Amani M., and Huang W., 2021, Oil spill detection based on multiscale multidimensional residual CNN for optical remote sensing imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 10941-10952. https://doi.org/10.1109/jstars.2021.3123163 Storto A., De Magistris G., Falchetti S., and Oddo P., 2021, A neural network-based observation operator for coupled ocean-acoustic variational data assimilation, Monthly Weather Review, 149(6): 1967-1985. https://doi.org/10.1175/MWR-D-20-0320.1
RkJQdWJsaXNoZXIy MjQ4ODYzNA==