基于GOCI数据的江苏近海营养盐遥感反演研究

Spatiotemporal variations of nutrients in Jiangsu coastal waters based on GOCI observations

营养盐是水质参数评价的重要指标,其浓度和结构直接影响海洋的生物群落,因此近海营养盐浓度准确监测及时空分布研究对海洋水质监测和海洋生态环境保护具有重要意义。本文基于江苏近海9个航次数据集,采用BP(Back Propagation)神经网络方法,输入包括GOCI卫星8波段遥感反射率数据及波段组合共14个模型参数,建立了无机氮及活性磷酸盐浓度反演模型。利用独立数据集对模型进行验证,结果表明模型精度良好。进一步将其应用于2011年至2020年的GOCI(Geostationary Ocean Color Imager)影像数据分析,获得了近10年江苏近海无机氮和活性磷酸盐浓度时空分布特征。研究显示,江苏海域营养盐呈现由近岸向外海递减的趋势,在外海呈现寡营养盐现象,且有明显的冬季高、夏季低的季节变化特征。此研究结果可为长期、便捷、大范围的水质监测研究提供方法支撑。

Nutrients serve as crucial water quality indicators,directly influencing marine primary productivity and ecosystems.Monitoring of the spatiotemporal distribution of nutrient concentrations is vital for marine quality assessment and ecological protection in coastal waters.A Back Propagation(BP)neural network-based models were proposed for estimating dissolved inorganic nitrogen and soluble reactive phosphorus concentrations using in situ measurements from nine cruise surveys along the Jiangsu coast.This new model utilizes 14 variables as inputs,including eight remote sensing reflectance bands(matched with GOCI(Geostationary Ocean Color Imager)data)and eight band combination forms.These two new models were independently validated and showed reliable inversion results.Furthermore,these models were applied to a decade’s GOCI satellite data,and the results indicate a decline in nutrient levels from nearshore to offshore waters and distinct seasonal fluctuations.These findings provide support for long-term,extensive water quality monitoring initiatives.