Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations
Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations
复制标题
根据卫星观测重建高分辨率海洋地下和内部温度和盐度异常
DOI：
10.1109/tgrs.2021.3109979
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发表时间：
2021
影响因子：
8.2
通讯作者：
Xiao-Hai Yan
中科院分区：
文献类型：
--
作者：
Lingsheng Meng;Chi Yan;Wei Zhuang;Weiwei Zhang;Xupu Geng;Xiao-Hai Yan
由于直接观测数据稀少且成本高昂，因此从遥感观测数据中准确反演海洋内部参数对于海洋和气候研究至关重要。此外，海水特性的高分辨率结构对于理解海洋过程和多尺度变化至关重要。本文设计了一种基于深度神经网络的太平洋次表层温度异常（STA）和次表层盐度异常（SSA）的高（1/4°）和超（1/12°）水平分辨率反演方法。我们利用了多卫星观测的海面数据（例如，海平面、温度、盐度和风矢量）作为输入。结果表明，我们的模型以高精度反演了高分辨率和超分辨率STA/SSA，并且该模型在广泛的深度（近地表至4000 m）和时间（2014年所有月份）范围内都是可靠的。关于高分辨率STA（SSA）估计，平均决定系数（R²）为0.984（0.966），平均均方根误差（RMSE）为0.068 °C（0.016 psu）。对于超分辨率STA，平均R²为0.988，RMSE为0.093 °C。本文建立了一种有效的方法，提高了卫星观测海洋内部参数估计的分辨率和精度。这项新技术为海洋观测和动力学提供了一些新的见解。
Accurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination (R²) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average R² was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.