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.

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

Marine oil spills pose a significant threat to ecosystems, highlighting the critical need for effective monitoring technology. Optical remote sensing technology plays a crucial role in monitoring marine oil spills. However, its performance is constrained by inherent trade-offs among temporal, spatial, and spectral resolutions, making it difficult for a single sensor to fully meet the demands of oil spill monitoring. Furthermore, existing oil spill detection algorithms often prioritize surrounding spatial features while neglecting the contribution of central spectral features, resulting in reduced detection accuracy. To address these issues, this paper proposes a joint framework for multi-sensor data spatial-spectral fusion and oil spill detection. This framework fuse images from the Coastal Zone Imager (50 m, 4 bands) with images from the Ultraviolet Imager and the Chinese Ocean Color and Temperature Scanner (1,000 m, 10 bands), all of which are onboard Haiyang-1C/D satellites, generating high temporal and spatial resolution ultraviolet-visible-near-infrared range images with 10 bands. The framework uses parallel branches, including a Convolutional Neural Network and a Vision Transformer, to extract surrounding spatial features and central spectral features from the fused data. This design enables the effective combination of fine-grained spatial information with multi-band spectral information, facilitating precise detection of oil spills in various emulsification states under different sun glint conditions. The proposed framework demonstrates strong performance, achieving F1-scores of 95.24% and 93.04% for detecting oil slicks and oil emulsions under weak sun glint conditions, and 90.06% for positive contrast oil spills under strong sun glint conditions. This study provides new insights for advancing oil spill monitoring and highlights the potential of multi-sensor data fusion in marine target detection.

Water clarity ( Z_SD ) is a critical water quality parameter that requires remote sensing mapping. Although great progress has been made in Z_SD retrieval over clear waters during past decades, challenges remain over turbid waters. To address this issue, a new model was proposed to retrieve Z_SD in clear oceanic to extremely turbid coastal waters, by improving the Z_SD retrieval in turbid waters. Firstly, waters were optically classified into three classes (clear, moderately turbid and extremely turbid waters) with band ratio of remote-sensing reflectance ( R_rs (λ)) f=R_rs (670)/ R_rs (490). Secondly, class-specific algorithms were adopted to retrieve the spectral diffuse attenuation coefficient K_d (λ) from R_rs (λ). Finally, Z_SD was semi-analytically estimated from minimum K_d (λ) in the visible domain. Data from oceanic and coastal waters (N=2260) were used for the model parameterization, test and validation. To demonstrate the model applicability to major satellite sensors, 1299 images from six spaceborne sensors were matched up with independent in situ Z_SD (N=1464, Z_SD =0.2-51 m) from global oceans. The results indicate that the new model has a good performance with mean absolute percentage error (MAPE) and Root Mean Square Difference (RMSD) of 21%-26% and 0.3-2.8 m. Even over extremely turbid waters, the model still performs robustly (MAPE=22%-25%) and significantly better than the existing ones. Finally, the model application indicates that the Z_SD derived from six sensors show good agreement in both spatial distribution and temporal consistency. The model shows the potential to construct high-accuracy Z_SD records from multiple sensors for global oceans and can support sustainable management of marine ecological environment.