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Red tide, one of the major ecological disasters in the world, exerts great impact on the marine environment. The ocean color satellite data with low spatial resolution and high spectral resolution is often used for red tide detection, but is insufficient for the fine-scale red tide detection due to its coarse spatial resolution. To address this problem, a new red tide detection method based on pseudo hue angle (PHA-RI) for high spatial resolution broad band satellite data is proposed in this paper. Different from the standard International Commission on Illumination (CIE) system, the false-color bands of near infrared (NIR), red and green are used to calculate the CIE tristimulus X, Y, Z, instead of the true-color bands of red, green and blue. With this method, the red tide can be easily differentiated from non-red tide water, and the distinction between red tide and non-red tide water is doubled compared to that of true-color bands composite. Experiment results show that the PHA-RI method can effectively detect red tide, with an averaged overall accuracy, F1-score and precision of 92%, 0.92 and 0.92 respectively. Compared with the traditional machine learning method support vector machine (SVM) and spectral index algorithm Gaofen-1 (GF-1) red tide detection index (GF-RI), the proposed algorithm has obvious advantages in red tide detection, especially for the strip distributed red tide. Besides, the proposed algorithm has good applicability, and it is proved to be suitable for the detection of red tides with different dominant species including Noctiluca scintillans , Skeletonema costatum and Heterosigma akashiide. Also, it is applicable to different broad band sensors, such as Chinese satellites GF-1 Wide Field of View (WFV), Haiyang 1D (HY-1D) Coastal Zone Imager (CZI), Sentinel-2 MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). The application experiment indicates that the proposed algorithm is effective in red tide detection, and it can successfully detect fine-scale red tide events, which have not been detected by Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua due to its coarse spatial resolution. The results also shows the advantages of high spatial resolution satellites in fine-scale red tide detection. This work provides a method for red tide detection using high spatial resolution broad band satellite data. [ABSTRACT FROM AUTHOR]

Abstract Since 2007, green tide blooms with Ulva prolifera as the dominant species have occurred every summer in the Yellow Sea. Biomass is a critical parameter used to describe the severity of green tide blooms. In this study, we analyzed the relationships between several indices (normalized difference vegetation index (NDVI), floating algae index (FAI), ratio vegetation index (RVI), enhanced vegetation index (EVI), ocean surface algal bloom index (OSABI), Korea Ocean Satellite Center (KOSC) approach) and the biomass per unit area of Ulva prolifera by using the in situ measurements from a water tank experiment. EVI, NDVI, and FAI showed strong exponential relationships with Ulva prolifera biomass per unit area. In order to apply the relationships to satellite remote sensing data, the impacts of the atmosphere (different aerosol optical depth at 550 nm) and mixed pixels to the relationships were analyzed. The results show that atmosphere has little effect on the relationship between EVI and Ulva prolifera biomass per unit area with R2 = 0.94 and APD (the average percentage deviation) = 19.55% when EVI is calculated from R rc (Rayleigh-corrected reflectance), and R2 = 0.95 and APD = 17.53% when EVI is calculated from R toa (top-of-atmosphere reflectance). Due to the low sensitivity to the atmosphere, the EVI relationship can be directly utilized in the top-of-atmosphere (TOA) reflectance without atmospheric correction. In addition, the EVI was slightly affected by mixed pixels with the APD only increased by ~10%. The EVI relationship was then applied to a long MODIS image time series to obtain the maximal total biomass of floating Ulva prolifera in the Yellow Sea from 2007 to 2016. The results showed that the maximum and minimum total biomass occurred in 2016 (~1.17 million tons) and 2012 (~0.074 million tons), respectively. The main factors that caused the inter-annual biomass variability were analyzed. The total amount of nutrients from Sheyang River which was the largest river on the northern coast of Jiangsu Province, and Porphyra cultivation in the Radial Sand Ridges of Jiangsu Province had both strong correlation with Ulva prolifera total biomass. Highlights • A new algorithm is proposed based on EVI which is most sensitive to biomass. • EVI is proved to be insensitive to aerosol optical thickness and mixed pixels. • The changes and influencing factors of biomass in recent ten years are revealed. [ABSTRACT FROM AUTHOR]

In this study, we monitored hourly changes in sea surface salinity (SSS) in turbid coastal waters from geostationary satellite ocean color images for the first time, using the Bohai Sea as a case study. We developed a simple multi-linear statistical regression model to retrieve SSS data from Geostationary Ocean Color Imager (GOCI) based on an in situ satellite matched-up dataset (R 2 = 0.795; N = 41; Range: 26.4 to 31.9 psμ). The model was then validated using independent continuous SSS measurements from buoys, with the average percentage difference of 0.65%. The model was applied to GOCI images from the dry season during an astronomical tide to characterize hourly changes in SSS in the Bohai Sea. We found that the model provided reasonable estimates of the hourly changes in SSS and that trends in the modeled and measured data were similar in magnitude and direction (0.43 vs 0.33 psμ, R 2 = 0.51). There were clear diurnal variations in the SSS of the Bohai Sea, with a regional average of 0.455 ± 0.079 psμ (0.02–3.77 psμ). The magnitude of the diurnal variations in SSS varied spatially, with large diurnal variability in the nearshore, particularly in the estuary, and small variability in the offshore area. The model for the riverine area was based on the inverse correlation between SSS and CDOM absorption. In the offshore area, the water mass of the North Yellow Sea, characterized by high SSS and low CDOM concentrations, dominated. Analysis of the driving mechanisms showed that the tidal current was the main control on hourly changes in SSS in the Bohai Sea. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment. [ABSTRACT FROM AUTHOR]

With marine pollution issues becoming serious and widespread, a series of coastal environmental managemental policies are being carried out worldwide, the effectiveness of which requires comprehensive evaluation. Taking the Bohai Sea (BS) of China as an example, which has been plagued by serious ecological and environmental issues for decades due to terrestrial pollution discharge, this study explored and quantified, for the first time to our best knowledge, the variability of water quality after initiating a dedicated 3-year pollution control action (Uphill Battle for Integrated Bohai Sea Management, UBIBM, 2018–2020) implemented by China's central government, with two water quality indexes of water color (Forel-Ule index, FUI) and transparency (Secchi disk depth, Z SD , m) from satellite observations. During the UBIBM, a significant improvement in water quality was detected, characterized by a clearer and bluer BS, with Z SD and FUI improved by 14.1% and 3.2%, respectively, compared with the baseline period (2011–2017). In addition, an abrupt drop in the long-term record (2011–2022) of the coverage area of highly turbid waters (Z SD ≤2 m or FUI ≥8) was found in 2018, which coincided with the start of the UBIBM, indicating that the water quality improvement may be attributed to the pollution alleviation of the UBIBM. Independent data of land-based pollution statistics also supported this deduction. (3) Compared with the previous two pollution control actions in the first decade of 21st century, UBIBM was proved to be the most successful one in terms of the achieved highest transparency and lowest FUI during the past two decades. Reasons for the achievement and implications to future pollution control are discussed for a more sustainable and balanced improvement in the coastal environment. This research provides a valuable example that satellite remote sensing can play a vital role in the management of coastal ecosystems by providing effective evaluation of pollution control actions. [Display omitted] • Nation-led coastal pollution control was evaluated by satellite remote sensing. • The BS turned clearer and bluer during the pollution control period (2018–2020). • The FUI and Z SD of the BS have improved to the status of the early 2000s. • The UBIBM is the most successful pollution control action over the past 20 years. [ABSTRACT FROM AUTHOR]