结合GOCI(Geostationary Ocean Color Imager)传感器的波段特征,基于缨帽变换方法设计了一种简单有效的绿潮指数(TCT-GTI)算法。结合目视判断的绿潮识别结果,将TCT-GTI算法与两种已有的遥感算法(AFAI和IGAG算法)监测结果进行对比发现,TCT-GTI算法的绿潮遥感监测结果精度较高,具有较高的可信度。将TCT-GTI算法应用到2017年多景GOCI影像,对中国黄海海域绿潮信息进行提取,同时分析绿潮覆盖面积的日变化特征,研究2017年绿潮暴发事件的漂移轨迹。研究结果表明,绿潮覆盖面积在中午12:00达到最大,这可能是光合作用等因素的影响。2017年,绿潮暴发事件经历了西北至东北的漂移路径,由江苏盐城外海海域向西北方向漂移至南黄海东部,然后继续向东北方向移动,抵达山东半岛南岸,逐渐消亡。

This study presents a simple and effective tasseled cap transformation based green tide index (TCT-GTI) algorithm by combining the band characteristics of the geostationary ocean color imager (GOCI). By analyzing the identification results of green tide from visual judgement, and comparing them with the monitoring results of two existing remote-sensing algorithms (AFAI and IGAG algorithms), we find that the TCT-GTI algorithm shows relatively high accuracy and reliability. In 2017, the TCT-GTI algorithm is applied to the extraction of green tide information in the Yellow Sea of China using the multi-view GOCI images. We analyze the daily variation characteristics of the green-tide coverage area and study the drift trajectory of the green tide event in 2017. The obtained results show that the green-tide coverage area reaches the maximum at 12:00, which may be influenced by factors such as photosynthesis. This bloom event of green tide in 2017 experiences a drifting trajectory from northwest to the northeast; i.e., it drifts from the offshore waters along Yancheng City in Jiangsu Province, to the east of the South Yellow Sea, then continues to move northeastward, reaches the south bank of the Shandong Peninsula, and gradually disappears.