An inversion method of subsurface thermohaline field based on deep learning and remote sensing data
An inversion method of subsurface thermohaline field based on deep learning and remote sensing data
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DOI：
10.1080/01431161.2023.2192880
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发表时间：
2023-04
影响因子：
3.4
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中科院分区：
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海水温度和盐度是海洋环境的基本参数，可以用来计算其他海洋环境参数。然而，大多数现场观测数据存在空间分布不均匀和时间不连续的问题，遥感观测方法难以获得地下信息。本文提出了一种深度学习模型，将遥感温盐数据与Argo剖面实测数据相结合，揭示了二者之间的非线性关系。实现了一种基于全球尺度精确点温盐遥感的水下三维温盐结构的直接反演方法。SST数据来自FY 3C-VIRR逐日海表温度产品，SSS数据来自SMAP Level 3 8天运行平均海表盐度产品，Argo散射数据来自“全球海洋Argo散射数据集”。基于数据的时空位置信息，将2016 - 2019年的遥感数据与Argo数据进行匹配，得到64751个有效配对点。深度学习模型被构造为具有5个隐藏层的多层感知器模型。温度的RMSE在130 m深度处最大值为2.106°C，在1000 m深度处最小值为0.367°C，验证数据集的平均值为1.174°C。盐度的均方根误差在0 m处最大，为0.356 psu，在1000 m处最小，为0.045 psu，平均值为0.202 psu。与其他基于固定网格产品的方法相比，该研究实现了全球海洋任意位置的直接反演方法，提高了反演精度，为精细化海洋监测提供了可靠的数据支持。
Seawater temperature and salinity are basic marine environmental parameters, which can be used to calculate other marine environmental parameters. However, most of the on-site observation data have the problems of uneven spatial distribution and time discontinuity, and it is difficult for remote sensing observation methods to obtain subsurface information. In this study, we proposed a deep learning model with combining remote sensing temperature and salinity as well as in-situ measured data by Argo profiles, and the nonlinear relationship was revealed. An effective and direct inversion method was realized for underwater three-dimensional thermohaline structure based on remote sensing temperature and salinity at accurate points on global scale. The SST data were obtained from the FY3C-VIRR daily sea surface temperature product, the SSS data were acquired from the SMAP Level 3 8-day running averages sea surface salinity product, and the Argo scatter data were got from the ‘Global Ocean Argo Scatter Data Set’. Based on the temporal and spatial location information of the data, this paper matched the remote sensing data from 2016 to 2019 with the Argo data, 64751 valid pairing points were obtained. The deep learning model was constructed as a multilayer perceptron model with 5 hidden layers. The RMSE of temperature had a maximum value of 2.106°C in 130 m depth and a minimum value of 0.367°C in 1000 m with an average of 1.174°C for validation dataset. And the RMSE of salinity had a maximum value of 0.356 psu in 0 m and a minimum value of 0.045 psu in 1000 m with an average of 0.202 psu. Compared with other methods based on fixed mesh products, this study realized a direct inversion method at any location in the global ocean and improved the inversion accuracy, which provides a reliable data support for refined marine monitoring.