基于遥感的珠江口表层盐度监测研究

Water Salinity in Pearl River Estuary Based on Remote Sensing

为加强河口地区咸潮活动规律的监测及拓展新型咸潮监测方法,以MODIS卫星影像数据和2010年～2011年多测次的水色因子浓度实测资料为基础,开展珠江河口表层盐度遥感监测研究。研究结果表明:在珠江河口水域,表层盐度与黄色物质呈负相关关系,建立基于黄色物质的珠江口表层盐度遥感定量反演模型可行;但仅依靠单参数黄色物质的表层盐度遥感模型存在稳定性差、反演精度波动大的问题,为此引入固定站点数据对模型进行优化,最终建立遥感数据与固定站点数据协同作用的表层盐度遥感自适应优化模型。模型验证结果显示优化模型对河口区表层盐度的监测误差控制在30%以内,具有较高实用价值。

A study was conducted in the Pearl River Estuary with the aim of developing a method for the retrieval of the surface salinity concentrations from remotely sensed ocean color.Firstly,using in-situ data collected in the Pearl River Estuary between 2010 and 2011,this paper analyzed the relationship between salinity and yellow substance originating and created a base model of estimation of surface salinity based on yellow substance(CDOM).Then the model was applied to MODIS data for retrieval of surface salinity.But the application result showed that the accuracy of the base model was not stable.In order to improve the stability of the model,a new thought that using the daily fix-site data to adjust the parameter of the model was proposed.Then a new optimization was established,which can estimate surface salinity using remote sensing based on the Fix-site data.Finally,through analyzing the operational precision of the new model,it showed that the estimated salinity is in close agreement with the in-situ data and the error is within 30%.