基于高时空分辨率光学遥感图像的第二模态内波探测方法研究

The second mode internal wave, which plays an important role in ocean mixing and energy exchange, have been frequently observed in South China Sea by the use of field observation methods. It also has a significant impact on marine engineering and submarine navigation. Therefore, the study of the second mode internal wave has important scientific significance and application prospects. Due to the small scale of the second mode internal wave, the commonly used SAR and MODIS are limited by the temporal and spatial resolution. So far, there is rare remote sensing-based study on the second mode internal wave. It is urgent to inverse the parameters of second mode internal wave by remote sensing..In this project, the optical remote sensing simulation experiment in the laboratory and deep learning method are innovatively combined to creat a novel method for obtaining inversed characteristic data of remote sensing images and the corresponding actual measured data set, which will effectively solve the difficulties in matching the inversed data with those measured data in reality, and establish an amplitude inversion model of the second mode internal wave. By taking full advantage of high-resolution optical remote sensing data obtained independently by our country, studies on the temporal and spatial features and propagation characteristics of the second mode internal waves in South China Sea are performed and finally realize the optical remote sensing detection of the second mode internal wave..This project will provide a new method for the amplitude inversion of the internal waves by remote sensing, and afford useful references for the application of deep learning technique in the study of ocean internal waves and other dynamic phenomena.

现场观测发现第二模态内波在南海多发，它对海洋混合、能量交换起到重要作用，也对海洋工程、潜艇航行带来重大影响，所以开展第二模态内波的研究具有重要科学意义和应用价值。由于第二模态内波时空尺度较小，常用的SAR和MODIS受时空分辨率限制，遥感探测第二模态内波的研究很少，特别是利用遥感反演第二模态内波参数的研究亟需解决。 . 本项目拟采用实验室光学遥感探测内波实验和深度学习方法相结合的创新技术，研究第二模态内波遥感图像特征参量与对应振幅数据集的获取方法，以克服海洋内波遥感图像与现场实测很难匹配的问题，实现第二模态内波振幅反演模型的构建；进而利用我国自主高分遥感数据研究南海东沙岛海域第二模态内波时空分布特征与传播特性。本项目的完成将为遥感反演内波振幅提供一种新方法，并为深度学习技术在研究海洋内波等动力现象中的应用抛砖引玉。

第二模态内波是海洋中的动力过程，其流核位于海面以下，对海洋混合、海上工程和军事国防都会产生重要影响，因此，对第二模态内波的研究具有科学意义和实际应用价值。可是，对第二模态内波的遥感观测研究还处于起步阶段，其与第一模态内波的特性差异仍需深入研究。随着光学遥感技术的不断进步，尤其是我国自主高分专项实施，高分一号和四号等卫星实现了高时空分辨率光学遥感观测，有助于对小尺度第二模态内波的光学遥感图像挖掘更多有效的信息。由于云雾和海况的影响，利用光学遥感图像反演第二模态内波振幅的模型还未见报道。因为在海洋中第二模态内波发生频次较低，导致现场实测和遥感观测数据少，特别是获得实测与遥感观测匹配的数据更加困难，因此就急需新方法解决这个难题。本项目在实验室率先创建了第二模态内波光学遥感探测仿真平台，实现了光学图像与第二模态内波波形一一对应，开辟了研究第二模态内波物理仿真的新方法；创新性发展了第二模态内波光学遥感成像机理的研究和基于机器学习技术建立了第二模态内波振幅反演模型；基于多年多源南海光学遥感图像完成了第二模态内波图像特征分析；此外，还利用美国麻省理工学院的通用环流软件建立了水槽中第二模态内波模型和发展了光纤传感内波探测技术。本项目的研究工作填补了光学遥感图像反演第二模态内波振幅的空白，推动了对海洋中高阶模态内波遥感观测的深入发展。

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