Abstract. Satellite remote sensing of sea surface chlorophyll products sometimes yields a significant amount of sporadic missing data due to various variables, such as weather conditions and operational failures of satellite sensors. The limited nature of satellite observation data impedes the utilization of satellite data in the domain of marine research. Hence, it is highly important to investigate techniques for reconstructing satellite remote sensing data to obtain spatially and temporally uninterrupted and comprehensive data within the desired area. This approach will expand the potential applications of remote sensing data and enhance the efficiency of data usage. To address this series of problems, based on the demand for research on the ecological effects of multiscale dynamic processes in the South China Sea, this paper combines the advantages of the optimal interpolation (OI) method and SwinUnet and successfully develops a deep-learning model based on the expected variance in data anomalies, called OI-SwinUnet. The OI-SwinUnet method was used to reconstruct the MODIS chlorophyll-a concentration products of the South China Sea from 2013 to 2017. When comparing the performances of the data-interpolating empirical orthogonal function (DINEOF), OI, and Unet approaches, it is evident that the OI-SwinUnet algorithm outperforms the other algorithms in terms of reconstruction. We conduct a reconstruction experiment using different artificial missing patterns to assess the resilience of OI-SwinUnet. Ultimately, the reconstructed dataset was utilized to examine the seasonal variations and geographical distribution of chlorophyll-a concentrations in various regions of the South China Sea. Additionally, the impact of the plume front on the dispersion of phytoplankton in upwelling areas was assessed. The potential use of reconstructed products to investigate the process by which individual mesoscale eddies affect sea surface chlorophyll is also examined. The reconstructed daily chlorophyll-a dataset is freely accessible at https://doi.org/10.5281/zenodo.10478524 (Ye et al., 2024).

中文翻译：

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使用 OI-SwinUnet 从 MODIS 每日重建南海叶绿素 a 数据集


摘要。由于天气条件和卫星传感器运行故障等各种变量的影响，海面叶绿素产品的卫星遥感有时会产生大量零星的缺失数据。卫星观测数据的有限性阻碍了卫星数据在海洋研究领域的利用。因此，研究卫星遥感数据重建技术以获得所需区域内时空不间断的综合数据非常重要。这种方法将扩大遥感数据的潜在应用并提高数据使用效率。针对这一系列问题，本文基于南海多尺度动力过程生态效应研究的需求，结合最优插值（OI）方法和SwinUnet的优点，成功开发了深度学习模型基于数据异常的预期方差，称为 OI-SwinUnet。采用OI-SwinUnet方法重建了2013年至2017年南海MODIS叶绿素a浓度产品。比较数据插值经验正交函数（DINEOF）、OI和Unet方法的性能时，显然，OI-SwinUnet 算法在重建方面优于其他算法。我们使用不同的人工缺失模式进行重建实验来评估 OI-SwinUnet 的恢复能力。最终，重建的数据集被用来检查南海各区域叶绿素a浓度的季节变化和地理分布。 此外，还评估了羽流锋对上升流区域浮游植物扩散的影响。还研究了重建产品的潜在用途，以研究单个中尺度涡流影响海面叶绿素的过程。重建的每日叶绿素-a 数据集可在 https://doi.org/10.5281/zenodo.10478524 上免费访问（Ye 等人，2024）。