基于Sentinel-2数据的近岸高悬沙水体漂浮绿潮遥感识别

黄海浒苔绿潮自2007年以来连年暴发。为筛选跟踪监测绿潮发生、发展过程的遥感识别方法，本研究使用Sentinel-2 MSI多光谱数据，在分析漂浮绿潮及背景高悬沙水体光谱特征的基础上，利用归一化植被指数（normalized difference vegetation index，NDVI）、漂浮藻类指数（floating algae index，FAI）、虚拟基线漂浮藻类指数（virtual baseline floating macroalgae height，VB-FAH）等多种遥感指数算法开展漂浮绿潮识别，比对分析了各算法的监测效果。结果显示，Sen2cor大气校正后地物光谱曲线峰谷特征显著，与实测光谱谱型的相似度明显提升；以近红外和绿光波段反射率差值为基础的VB-FAH指数算法可在一定程度上降低高悬沙水体对近红外和红光通道反射率的影响，漂浮绿藻识别效果比NDVI及FAI指数更好。本研究可为高悬沙水体漂浮绿潮遥感识别提供技术支持。

Remote sensing identification of floating green tide in offshore high suspended sediment water based on Sentinel-2 imagery

The Yellow Sea enteromorpha green tide occured year after year since 2007. In order to trace the source and development process of green tide, various remote sensing identification methods have been carried out. In this study, Sentinel-2 MSI multispectral data were used to identify floating green tide. Firstly, field-measured spectrum of the floating green tide and the suspended sediment water was analyzed. And then three remote sensing index methods, NDVI, FAI, VB-FAH, were applied into floating green tide identification. Finally, comparison between remote sensing identification and manual extraction was proceeded. Compared with NDVI and FAI, VB-FAH index based on the reflectance difference between near-infrared and green bands, which can reduce the influence of high suspended sediment water on near-infrared and red channel, showed a better identification effect. This study can provide technical support for remote sensing identification of floating green tide in high suspended sediment water.