渤海海表面盐度神经网络反演研究
Retrieval of Sea Surface Salinity in the Bohai Sea by Neural Network
刘振宇 1孙伟富 2陈磊3
作者信息
- 1. 中南民族大学 资源与环境学院,湖北 武汉 430074
- 2. 自然资源部第一海洋研究所 海洋物理与遥感研究室,山东 青岛 266061
- 3. 中国科学院海洋研究所,山东 青岛 266071;中国石油大学(华东),山东 青岛 266580;中国科学院海洋大科学研究中心,山东 青岛 266071
- 折叠
摘要
海表面盐度是研究海洋生态环境和全球气候变化的重要指标.基于多层神经网络,利用实测海表面盐度数据和 MODIS-Aqua 遥感反射率产品,针对渤海建立了海表面盐度的反演模型,随后通过该模型分析了 2022 年 3 月至 2023 年 3 月 4 个季度的渤海盐度时空变化.研究发现:模型的决定系数(R2)和均方根误差(RMSE)分别为 0.66 和 0.39,优于先前文献提出的多波段线性模型(0.39 和 0.60).渤海盐度的时空分析表明:黄河冲淡水是影响渤海海表面盐度重要因素,它致使莱州湾和辽东湾的盐度长期降低;9 月份以后随着渤海冷流沿山东半岛南下,随后向渤海海峡和北黄海方向扩散.
Abstract
Sea surface salinity is an important indicator for studying marine ecological environments and global climate change.Based on a multi-layer neural network,a retrieval model for sea surface salinity in the Bohai Sea is established by using measured sea surface salinity data along with MODIS-Aqua remote sensing reflectance product.Subsequently,the spatiotemporal variations of salinity in the Bohai Sea over four quarters from March 2022 to March 2023 are investigated by mapping the salinity distribution of the Bohai using this model.The results show that the coefficient of determination(R2)and root mean square error(RMSE)of the model are 0.66 and 0.39 respectively,which are better than the previously proposed multi-band linear model(0.39 and 0.60).The mapping of Bohai Sea salinity reveals that the dilution effect of the Yellow River freshwater is a significant factor affecting the sea surface salinity,resulting in a long-term decrease in salinity in Laizhou Bay and Liaodong Bay.After September,followed as the Bohai Cold Water Mass,the Yellow River freshwater moves southward along the Shandong Peninsula,subsequently disperses towards the Bohai Strait and the northern Yellow Sea.
关键词
海表面盐度/渤海/神经网络/MODIS-AquaKey words
sea surface salinity/the Bohai Sea/neural network/MODIS-Aqua引用本文复制引用
基金项目
国家自然科学基金重点项目(61931025)
出版年
2024
国家科技期刊平台
NSTL
万方数据