## 1 Introduction  
In 2007, a small-scale green tide formed by _Ulva prolifera_ was observed for the first time in the coastal area of Qingdao, China [1]. However, the free-floating macroalgae appeared again during the Olympic Games in 2008, covering approximately 13,000-30,000 km\({}^{2}\) and posing a great threat to the Olympic sailing competition [2]. With intensive efforts, over 16,000 people and 600 boats were organized to clean up the coastal water for sailing training, resulting in substantial economic losses [2; 3; 4; 5]. Since then, the world's largest macroalgal bloom has become a continuous phenomenon in the southern waters of the Yellow Sea, and it has attracted great attention for marine researchers. Also, _Ulva prolifera_ has been confirmed as the only dominant species in the Yellow Sea green tides, by both morphology and molecular analyses [6; 7; 8]. Because of the sudden outbreak of green tide and its migration along with the wind direction and tide, it is difficult to meet the needs for disaster prevention and mitigation by the traditional sampling method of ship surveys. Remote sensing (RS) technology has the advantage of fast data acquisition, large coverage, and dynamic monitoring. It can monitor the origin, spatial distribution,dynamic process, scale, and migration of green tides in real time. It has become one of the irreplaceable means for green tide monitoring and early warning. A mass of past studies have documented the appearance, statistics, and causes of _U. prolifera_ through RS. Using satellite tracking methods, the world's largest green tide in the Yellow Sea was reported to grow every year in late spring, with a peak in June, and finally to disappear in August [1; 2; 9; 10; 11]. Most researchers believe that Subei Shoal serves as the \"cradle\" of large-scale green tides, and this hypothesis was confirmed by previous studies [2; 10; 12; 13]. Over the years, the maximum coverage and water distribution area during the bloom season were recorded to show the severity of _U. prolifera_[12; 14; 15], which varied in different years. On an annual scale, mean _U.l.v._ coverage decreased after 2008, but it increased rapidly after 2012 from 8 km\({}^{2}\) in 2012 to 116 km\({}^{2}\) in 2015 [11]. Meanwhile, the transport patterns of free-floating algae were numerically modeled and validated by satellite observations. Under wind-current action, the algae drifted from Jiangsu Province northward to the offshore region of Qingdao, Shandong Province [10; 16], which has become the most severely affected area in recent years. To explore the interannual variation of _U. prolifera_, researchers have used various remote-sensing images and various image recognition methods, but mostly from the viewpoint of the distribution range, coverage area, or migration trajectory. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a 250 m spatial resolution are the most popular, and were used by [PERSON] et al. [2] to quantify the covered and affected areas of _U. prolifera_. [PERSON] et al. [17] compared the imaging ability of multiple data sources, including MODIS, HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting), charge-coupled devices (CCDs), Environmental Satellite (ENVISAT), and Advanced Synthetic Aperture Radar (ASAR) for macroalgae monitoring and drift velocity estimation. Using the Geostationary Ocean Color Imager (GOCI), [PERSON] et al. [18] tracked the pathway of floating green algae blooms. More recently, MODIS estimates were compared for the first time with concurrent high resolution (3 m) airborne synthetic aperture radar (SAR) data by [PERSON] et al. [19], with the purpose of assessing and refining our knowledge of green macro-algal coverage. The Normalized Difference of Vegetation Index (NDVI) may be the most commonly used index for detecting _U. prolifera_ in sea areas [20].