Methods for detecting green tide in the Yellow Sea using Google Earth Engine platform

Abstract

The green tide was a blooming phenomenon consisting of outbreaks and aggregations of marine macroalgae, causing severe damage to the ecosystem of the Yellow Sea in China. Remote sensing technology was considered an effective means of green tide detection. Currently, commonly used low spatial resolution satellites cannot satisfy the green tide detection in the fine-scale range. To better improve the accuracy of green tide identification, based on the Commission Internationale del'éclairage (CIE) system, two new green tide detection algorithms (CIE xy, hue angle α) were established using Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data on the Google Earth Engine (GEE) platform. These algorithms were validated by introducing the quantitative index Dgw from "Interclass Distance". The CIE xy algorithm and the hue angle α algorithm based on Sentinel-2 MSI exhibited higher Dgw values of 10.29 and 5.87, respectively, surpassing the performance of the two algorithms based on the Landsat-8 OLI in green tide identification (Dgw values of 3.36 and 5.17, respectively). Meanwhile, the proposed algorithms were compared with normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio vegetation index (RVI), floating algae index (FAI), enhanced vegetation index (EVI), and algal bloom detection index (ABDI) algorithms using Sentinel-2 MSI images. The results showed that the CIE xy algorithm and the NDVI algorithm outperformed the other algorithms. Especially in turbid water, the CIE xy algorithm exhibited significant advantages. However, the hue angle α algorithm still showed some potential in identifying green tide, particularly in the presence of thin cloud interference. These findings can provide effective information support for the monitoring of green tide in the Yellow Sea.