The green tide caused by Ulva prolifera (U. prolifera) is becoming more severe as climate change and human activity accelerate, endangering tourism, aquaculture, and urban landscapes in coastal cities. In order to understand the spatio-temporal distribution of U. prolifera in response to the green tide disaster, this study used the Haiyang-1C (HY-1C) satellite accompanied by the Sentinel-2 and GaoFen-1 (GF-1) satellites to systematically monitor U. prolifera between 2020 and 2022. The consistency of U. prolifera distribution between the HY-1C and Sentinel-2 satellites, as well as the HY-1C and GF-1 satellites, was first investigated and the determination coefficients (R²) were 0.966 and 0.991, respectively, which supports the feasibility of China’s first ocean water color operational satellite, HY-1C, for U. prolifera monitoring. Therefore, the spatio-temporal distribution of U. prolifera is studied herein, along with the influence range, influence area, and drift paths. From 2020 to 2022, U. prolifera appeared in late May and lasted for 61, 88, and 73 days. Additionally, the in influence area continuously decreased in 2020 and 2022, while it generally increased and then decreased in 2021. It is an interesting phenomenon that when the maximum influence area occurred at the early stage of U. prolifera in both 2020 and 2022, the drift paths tended to move southward after traveling northward. The overall trend of the drift path in 2021 was to head northward. Thus, the study of the dynamic evolution, influence range, influence area, and drift paths of U. prolifera is helpful to promote the systematic development of emergency response mechanisms for U. prolifera.

Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and cause ecological, environmental, and economic problems. To understand MAB dissipation features, we used multiple sensors to analyze the spatiotemporal variation of the MAB dissipation phase in the southern Yellow Sea. The results show the variation in the daily dissipation rate (DR) was inconsistent from year to year. Based on the DR variation, a simple method of estimating MAB dissipation days was proposed for the first time. Verification results of the method, from 2018 to 2020, showed the estimated dissipation days were relatively consistent with the results obtained by remote sensing imagery. From 2007 to 2020, the order in which macroalgae landed in the coastal cities of Shandong Peninsula can be roughly divided into two types. In one type, the macroalgae landed first in Rizhao, followed by Qingdao, Rushan, and Haiyang. In the other type, they landed in the reverse order. The MABs annual distribution density showed significant differences in the southern Yellow Sea. These results provided a basis for evaluating the MABs’ impact on marine ecology and formulating the green-tide prevention and control strategies.

Ulva prolifera and Sargassum are two common floating macroalgae in China’s coastal algal bloom events. Ulva prolifera frequently emerges concomitantly with Sargassum outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between Ulva prolifera and Sargassum, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between Ulva prolifera and Sargassum are observed. This study proposes a random forest-based algorithm for discriminating between Ulva prolifera and Sargassum in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing Ulva prolifera and Sargassum within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm’s robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between Ulva prolifera and Sargassum. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation.

Water transparency is one of the important parameters to describe the optical properties of seawater. It's a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. Firstly, this paper verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Secondly, the water transparency estimation results retrieved from GOCI (Geostationary Ocean Color Imager) using four atmospheric correction algorithms were compared and analyzed with in-situ data in study area. The four atmospheric correction algorithms are the standard atmospheric correction algorithms of KOSC GOCI GDPS1.3 and GDPS2.0, the standard near-infrared atmospheric correction algorithm of NASA, and MUMM (Management Unit of the North Sea Mathematical Models). At last, GOCI data on Mar 09, 2018 were used to analyze the differences of hourly changes of transparency caused by different atmospheric correction algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R ² >0.8, MAPE = 16.4%). The difference of transparency value caused by atmospheric correction algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency presented based on different atmospheric correction algorithms, while the moment when the minimum value of transparency presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different atmospheric correction algorithms are not completely consistent.

The differences in spatial resolution between multi-source images can result in noticeable variations in estimations of the area covered by macroalgae. Even methods like pixel un-mixing and relational models cannot eliminate this issue entirely. The Huanjingjianzai-2A/2B (HJ-2) satellite’s wide-view charge-coupled device (CCD) camera provides fine spatial resolution (16 m) and high temporal resolution (2 days), effectively resolving the problems associated with using images of different resolutions. This paper evaluated the macroalgae detection capability of HJ-2 CCD and analyzed the spatiotemporal variations of macroalgae blooms (MABs) in 2022 based on HJ-2 imagery. The results indicated that the macroalgae detection capability of HJ-2 CCD was on par with that of GF-1 WFV. The spatiotemporal variation of MABs in 2022 was similar to the variation of previous years. The annual distribution density level (representing the degree to which MABs were affecting the Yellow Sea) offshore of Rizhao and Lianyungang during the dissipation phase in 2022 was higher than in the previous years. These results support the potential of utilizing high-resolution remote sensing for the dynamic monitoring of MABs in terms of both spatial and temporal aspects.