As a marine ecological disaster caused by the explosive proliferation of green macroalgae, green tides impair economic development and the ecological environment, affecting dozens of regions worldwide. The largest green tide in the world occurs in the Yellow Sea, with Ulva prolifera ( U. prolifera ) the dominant species. Satellite remote sensing technology, with its advantages of a large scale, a long time series, and traceability, plays a significant role in U. prolifera monitoring, providing important support for obtaining deeper scientific understanding and promoting disaster prevention and control. To systematically and comprehensively summarize research progress and identify weaknesses and priorities for future development, this article reviews over 350 articles on U. prolifera green tide remote sensing in the Yellow Sea, published before ­November 2023 from three aspects: remote sensing ­mechanisms (electromagnetic scattering and remote sensing image features), methods (detection, coverage area retrieval, species discrimination, biomass estimation, drift velocity determination, and so on), and applications (growth and decay, interannual variabilities, and so forth). Additionally, challenges, opportunities, and development priorities are analyzed (see “Article Contents”). The findings in this article promote the future development of U. prolifera remote sensing technology to assist with disaster prevention and ecosystem protection.

Most existing endmember extraction techniques require prior knowledge about the number of endmembers in a hyperspectral image. The number of endmembers is normally estimated by a separate procedure, whose accuracy has a large influence on the endmember extraction performance. In order to bridge the two seemingly independent but, in fact, highly correlated procedures, we develop a new endmember estimation strategy that simultaneously counts and extracts endmembers. We consider a hyperspectral image as a hyperspectral pixel set and define the subset of pixels that are most different from one another as the divergent subset (DS) of the hyperspectral pixel set. The DS is characterized by the condition that any additional pixel would increase the likeness within the DS and, thus, reduce its divergent degree. We use the DS as the endmember set, with the number of endmembers being the subset cardinality. To render a practical computation scheme for identifying the DS, we reformulate it in terms of a quadratic optimization problem with a numerical solution. In addition to operating as an endmember estimation algorithm by itself, the DS method can also co-operate with existing endmember extraction techniques by transforming them into a novel and more effective schemes. Experimental results validate the effectiveness of the DS methodology in simultaneously counting and extracting endmembers not only as an individual algorithm but also as a foundation algorithm for improving existing methods. Our full code is released for public evaluation. 1

In 2017, large-scale macroalgae blooms with different dominant species of Ulva prolifera and Sargassum occurred concurrently in the Yellow and East China Seas, which poses a challenge to the cognition and control of macroalgae disaster. Therefore, it is necessary to develop an algorithm to distinguish U. prolifera and Sargassum from satellite images. In this study, the spectral difference between U. prolifera and Sargassum and the capability of several multispectral satellite missions to distinguish them is first analyzed. The results show that the reflectance peak in visible wavelength is always in ~550 nm for U. prolifera whether it is floating in clear open water or turbid nearshore water. However, the reflectance of Sargassum floating in clear and turbid water shows totally different characteristics, because most of Sargassum body is submerged in the water and the observed Sargassum reflectance is seriously affected by water reflectance. Compared with Landsat 8 Operational Land Imager (OLI), HuanJing-1, Charge-Coupled Devices (HJ-1 CCD), Aqua Moderate-resolution Imaging Spectroradiometer (MODIS), and Sentinel 2 Multi-Spectral Instrument (MSI), GaoFen-1, Wide Field of View (GF-1 WFV) can preferably capture the spectral difference between U. prolifera and Sargassum . Based on the spectral difference analysis, we propose a random forest-based algorithm to distinguish U. prolifera and Sargassum from GF-1 WFV images with an overall accuracy of 97.6% except when U. prolifera and Sargassum mix together. The algorithm is more robust than the existing ones as it allowed more Sargassum samples from different ocean regions to be used in the training; in addition, it avoids negative effects caused by the selection of a threshold. The proposed algorithm is proved effective in distinguishing U. prolifera and Sargassum in the Yellow and East China Seas in May and June 2017 and in detecting Sargassum in the Atlantic Ocean. Thus, this method can be used in researches including floating macroalgae traceability and competition and succession between different macroalgae species in different regions of the ocean with similar environments.

In this study, sea surface skylight spectral reflectance ρ(λ) was retrieved by means of the non-linear spectral optimization method and a bio-optical model. The spectral variability of ρ(λ) was found to be mainly influenced by the uniformity of the incident skylight, and a model is proposed to predict the ρ(λ) spectral dependency based on skylight reflectance at 750 nm. It is demonstrated that using the spectrally variable ρ(λ), rather than a constant, yields an improved agreement between the above-water remote sensing reflectance R_rs(λ) estimates and concurrent profiling ones. The findings of this study highlight the necessity to re-process the relevant historical above-water data and update ocean color retrieval algorithms accordingly.