Long-term, high-quality in situ observations across diverse aquatic and atmospheric environments are critical for validating ocean color satellite sensor data products. We established the Long-term Ocean and Atmosphere Simultaneous Observation Network (LOASON) for supporting the validation of ocean color satellite data products, a network of eight strategically distributed fixed sites along the Chinese coastline. LOASON spans approximately 80% of the global water type classifications and encompasses the full range of aerosol regimes observed worldwide. LOASON enables the measurement of a comprehensive suite of optical parameters, including normalized water-leaving radiance (Lwn), aerosol optical depth (AOD), & Aring;ngstro & uml;m exponent (AE), and detailed aerosol microphysical properties such as size distribution, single scattering albedo, and refractive index. Long-term observations at LOASON sites demonstrate that the network effectively complements the global ocean color component of the Aerosol Robotic Network (AERONET-OC) infrastructure, particularly in optically complex coastal environments where aerosol and water optical properties are highly variable. Validation exercises using LOASON data across multiple satellite platforms, including Visible Infrared Imaging Radiometer Suite (VIIRS), Chinese Ocean Color and Temperature Scanner (COCTS), and Ocean and Land Color Instrument (OLCI), revealed consistent performance trends. Mean absolute percentage errors (MAPEs) typically remained around 20% in the blue and red spectral bands and less than 10% in the green bands, closely mirroring validation outcomes reported at AERONET-OC reference sites. These findings confirm that the data quality and validation capabilities of LOASON are in line with established global reference networks, supporting its emerging role as a robust, reliable infrastructure for the long-term validation and improvement of ocean color satellite sensor data products.

Ocean color satellites are important tools in the field of environmental remote sensing, and their calibration accuracy during in-orbit operation is crucial. This study introduces a radiometric calibration method based on a hyperspectral reference source, aimed at evaluating discrepancies in onboard absolute radiometric calibration among different ocean color satellite sensors and providing more consistent ocean color remote sensing products. Specifically, the method uses a high-precision hyperspectral ocean color sensor, satellite calibration spectrometer (SCS), as a unified reference source to obtain correction coefficients for multiple sensors through radiometric calibration. Leveraging the hyperspectral capabilities of the SCS, the absolute radiometric calibration differences between sensors can be clearly analyzed after removing variations in the relative spectral response (RSR) functions. Simultaneously, the study employs the simultaneous nadir overpass (SNO) method to match the sensors, analyzing the factors that influence the SNO process. The study reveals that despite accounting for RSR differences, significant radiance disparities remain. The largest overall difference among five sensors reached 6.37%. However, applying the derived correction coefficients enhances the consistency of ocean optical and biogeochemical products in the global open ocean. Based on the results of the time series analysis, the average consistency of multisource satellite-derived chlorophyll-a (Chla) concentration products improved from 73.7% to 90.4%. This result demonstrates the effectiveness and necessity of the calibration evaluation method proposed in this study for enhancing the consistency of multisource data. Furthermore, this method can be applied to additional ocean color sensors in the future, as long as the necessary matching conditions between satellites are met.

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater-sea surface-air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from -114 dB to -157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments.

At about 21:00 on April 20, 2021 (UTC), the Indonesian Navy submarine "KRI Nanggala-402" lost contact in the north of Bali Island and eventually sank. To explore the impact of internal waves (IWs) on the sinking of the "Nanggala" submarine, an IW parameter inversion method from the synthetic aperture radar (SAR) image based on an Euler numerical model is proposed. The IW amplitude and propagation speed, 25.2 m and 2.36 m/s, respectively, at the wreck site are retrieved from a Sentinel-1A/SAR image acquired two days before the losing contact using the Euler numerical model with the assistance of geostationary satellite Himawari-8 data. Further considering the barotropic tide current variation trends in the Lombok Strait (LS), the amplitude there was no more than 25 m when the submarine sank. The IWs with such small amplitude could contribute little to the submarine sinking. Therefore, we do not consider IWs as the primary cause of this submarine sinking accident.

System vicarious calibration (SVC) is essential for achieving high-precision ocean color products from satellite sensors, traditionally relying on in situ measurements from stable calibration sites. However, data constraints due to environmental factors have prompted the exploration of marine pseudo-invariant sites (MPISs) as supplementary SVC sources. This study investigates the feasibility of MPIS for SVC by analyzing long-term Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua data to identify oceanic regions with stable optical properties suitable for calibration. Four MPISs were identified, and time-series models of remote sensing reflectance ( $R_{\mathrm {rs}}$ ) were constructed, demonstrating consistent variability patterns that support their suitability for SVC. These models were then applied to calculate SVC coefficients for Visible Infrared Imaging Radiometer Suite (VIIRS)-suomi national polar-orbiting partnership (SNPP) and were validated against in situ measurements from the Marine Optical Buoy (MOBY) site. Results indicate that MPIS-derived SVC coefficients show less than 1.68% discrepancy across visible (VIS) wavelengths compared to MOBY, enhancing SVC stability and reducing calibration timeframes. Additionally, the comparison between VIIRS-derived remote sensing reflectance, before and after applying the MPIS-based SVC coefficients, and in situ data further verifies the accuracy of the calibration coefficients. This study underscores MPIS's potential to supplement traditional SVC methods, improving calibration coverage and enabling continuity in ocean color remote sensing missions.

Objective Accurate measurement of seawater absorption coefficients is important for ocean radiative transfer simulations, biogeochemical parameter inversion, and calibration and validation of ocean color satellites. Reflective-tube absorption meters are the most commonly employed instruments for seawater absorption coefficient measurement, but the measured absorption coefficient must be corrected for scattering due to instrumental design limitations. There have been a few studies evaluating the ac-9/ac-s scattering correction methods. However, there is a lack of application evaluation of these correction methods applied to the coastal waters of China. We evaluate the ac-9/ac-s scattering correction methods based on field measurements from the coastal waters of China's sea. Based on the evaluation, guidance is provided for the selection of scattering correction methods when reflective-tube absorption meters are employed in different water bodies. Methods We introduce five scattering correction methods to evaluate their performance on the field measured data from the Bohai Sea, Yellow Sea, East China Sea, and South China Sea. The data includes the absorption coefficient measured by the reflective-tube absorption meters of ac-9, ac-s, and the point source integrating cavity absorption meter (OSCAR), the backscattering coefficients measured by the backscattering instrument HS 6, the volume scattering function measured by LISST-VSF and the temperature, salinity, and depth data measured by CTD. The absorption coefficients are measured by adopting ac-9 and ac-s for two cruises to independently evaluate the performance of different scattering correction methods applied to ac-9 and ac-s. In the South China Sea, the absorption coefficient measured by OSCAR is taken as the true value to evaluate the application of scattering correction methods to the ac-s/ac-9 measurements. In the Bohai Sea, Yellow Sea, and East China Sea, there is no synchronous OSCAR measurement. Meanwhile, the absorption coefficient corrected by the volume scattering correction method is utilized as a reference for evaluating other scattering correction methods. Since the volume scattering function is measured independently, the error obtained by integrating it in the limited angle is the scattering error measured by the reflection-tube absorption coefficient measurement instrument, thereby making this method a more accurate scattering correction method. Prior to each cruise, all instruments have undergone rigorous calibration, including pressurized flow ultra-pure water calibration for ac-s and ac-9 in the laboratory, integrator cavity reflectivity calibration for OSCAR, and calibration for HS6 to ensure the accuracy of field measurements. Results and Discussions The results show that for the clean water in the South China Sea, little difference is found among different scattering correction methods. The baseline method and volume scattering correction method have better correction effects, and the relative errors after data correction are 25.05%and 23.24%respectively. The relative error of the semi-empirical correction method is 36.01%. The performance of the proportional method and iteration correction method is poor. In the Bohai Sea, the proportional method and semi-empirical correction method perform better, with relative errors of 29.22%and 25.02%respectively. After the correction of the baseline method and iterative method, the correction results of each band have a large deviation from the reference value. In the Yellow Sea, the proportional method is relatively sound, with 23.17%of the relative error. The baseline method and the semi-empirical correction method are similar, and the relative error after data correction is 30.94%and 31.68%respectively. In the East China Sea, the semi-empirical correction method has the best correction effect, and the relative error after data correction is 14.71 degrees o, followed by the proportional method. Additionally, the relative error after data correction is 24.02 degrees o, and the baseline method performs slightly worse. Conclusions We evaluate five representative scattering correction methods for reflective-tube absorption coefficient measurement based on field data from several regions of China's sea. These methods include the baseline method, proportional method, semi-empirical correction method, iterative correction method, and volume scattering function correction method. Generally, all these scattering correction methods can reduce the scattering error from reflective tubes, and make the absorption coefficient approximate to the true value. However, the performance of each method varies between different types of water. Based on the analysis results of all the data, our suggestions for selecting scattering correction methods for the reflective-tube absorption meters are as follows. The baseline method is more suitable for clean water bodies (the South China Sea), the semi-empirical correction method is suited for turbidity water (the Bohai Sea, the Yellow Sea, the East China Sea, and coastal water), and the volume scattering method is preferred if the measured volume scattering function of water is available.

Channel modeling of seawater is essential for understanding the transmission process of underwater laser light and optimizing the system design of underwater wireless laser communication. This study systematically examined the transmission characteristics of underwater blue-green laser communication, such as the angle of arrival, beam spreading, and channel loss, based on the Monte Carlo ray tracing method, across three different waters. The statistical analysis has led to the following definitive conclusions: (a) The differences in average AOA are profound in clear water and at short attenuation lengths in coastal and turbid harbor waters and are small at long attenuation lengths. The differences in average AOA between the offsets of 0 m and 10 m are about 62.3° and 12.9° at the attenuation lengths of 1 and 25 in clear water. The differences between offsets of 0 m and 10 m in average AOAs are about 74.4° and 5.8° in coastal water and 67.2° and 12.2° in turbid harbor water at the attenuation lengths of 1, 20, and 35, respectively. (b) The beam diameters are 0.1 m at the attenuation length of 25 in clear water and 83.8 m and 25.3 m when the attenuation length is 35 in coastal and turbid harbor waters. It manifests that the beam spreading is indistinctive in clear water while prominent in coastal and turbid harbor waters. (c) The difference in the received power at the various offsets decreases with increasing attenuation length but with distinct patterns. Take the offsets of 0 m and 10 m as examples. The absolute difference in the power loss reduces from 88.0 dB·m−2 to 46.8 dB·m−2 when the attenuation length reaches 25 in clear water. At the attenuation lengths of 1 and 35, the power losses are 94.9 dB·m−2 and 4.3 dB·m−2 in coastal water and 117.4 dB·m−2 and 12.6 dB·m−2 in turbid harbor water. Moreover, the minimum underestimation of power loss by applying Beer’s Law could be almost 2 dB·m−2 in turbid harbor waters. To achieve a high receiving gain, the weighted average angles of arrival at different offsets indicate that a small field of view is advantageous in clear water and at short transmission distances in coastal and turbid harbor waters. In contrast, a larger field of view is effective at long transmission distances in coastal and turbid harbor waters. Additionally, the absolute differences in channel losses at various offsets suggest that alignment between the transmitter and the receiver is crucial in clear water and at short transmission distances in coastal and turbid harbor waters. In contrast, misalignment may not lead to significant channel loss at longer transmission distances in turbid harbor water. The results of this study underscore the importance of considering water type, transmission distance, and offsets relative to the beam center when selecting receiver parameters.

Abstract To achieve the integration of multiple ocean color (OC) sensors’ radiometric calibration tasks into a single system, we have developed an intelligent on-orbit radiometric calibration system called the Generalized Radiometric Calibration Entity for Ocean Color (Grace-OC). The system features real-time data downloading capabilities and integrates three calibration methods: onboard calibration, system vicarious calibration and cross calibration, enabling intelligent selection of on-orbit radiometric calibration methods tailored to the calibration sensors. Compared to other calibration systems, we have improved the universality and efficiency of the system by establishing high-spectral aerosols and Rayleigh lookup tables (LUTs) which are verified consistency through a comparative analysis with operational LUTs releasing by National Aeronautics and Space Administration (NASA) in this paper. Building upon this foundation, we have integrated a comprehensive analysis function for calibration coefficients to automatically construct degradation models of the radiometric measurement performance, and to achieve mutual verification between different calibration methods. We applied Grace-OC to HY1C/D and verified the feasibility of the intelligent selection calibration methods and the stability of the calibration system, achieving a calibration accuracy of up to 0.5%. Simultaneously, the precision of degradation models of the radiometric measurement performance is confirmed through Grace-OC, and the on-orbit radiometric calibration task was ultimately completed within 6 minutes for each per scene. Based on the above applications, Grace-OC has demonstrated its universality for various OC sensors, as well as the stability of on-orbit radiometric calibration tasks and the efficiency of operational speed.

Sunglint significantly impacts the extraction of ocean color information, particularly for sensors lacking tilt capabilities. Traditional atmospheric correction algorithms often fail to retrieve effective data in high-sunglint regions. The polynomial-based POLYMER method, applied to MERIS data, effectively addresses sunglint, although its accuracy decreases by about 15% in such conditions. To enhance data reliability in sunglint regions, we propose the Improved polynomial nonlinear optimization approach (IPNOA), a revision of the POLYMER atmospheric correction. IPNOA employs the QAA-RGR (quasi-analytical algorithm-red-green-bands-ratio) to refine the bio-optical ocean reflectance model. Additionally, due to the nonlinear optimization algorithm’s sensitivity to initial values, this study uses global 8-day average oceanic optical properties at 4 km resolution as the initial setting. The performance of IPNOA was initially evaluated using a synthetic dataset, with retrieved remote sensing reflectance (Rrs) closely matching the simulated Rrs across all wavelengths. The mean absolute percentage error (MAPE) remained below 1% for non-sunglint, moderate sunglint, and high sunglint conditions. Further analysis of in situ data revealed that IPNOA performs better, exceptionally at 412 nm, with a MAPE of 5.27% in sunglint regions. When processed by POLYMER, the dataset exhibited a MAPE of 68.47%. Finally, an analysis of global data from MODIS, VIIRS, and HY1C/D on July 15, 2022, showed good agreement among the three on a global scale. Above all, these results indicate that the IPNOA algorithm has strong potential for retrieving valid products in moderate, even high sunglint regions, offering practical benefits for expanding the spatial coverage of ocean color satellite data.

The construction of a radiometric degradation model is vital for elucidating the performance alterations of ocean color satellite sensors following in-orbit operations. The Chinese ocean color satellites HaiYang-1C (HY1C) and HaiYang-1D (HY1D), operating as afternoon and morning satellites, respectively, conduct networked observations of the global ocean. However, there has been limited analysis of the radiometric performance of each ocean color sensor on HY1C/D. Therefore, to understand the performance change sensor, this study relies on the satellite calibration spectrometer (SCS), a hyperspectral sensor mounted on HY1C/D. The radiometric degradation model of each sensor is constructed, to realize the tracking of the long-term radiometric performance trend of the sensors. First, the SCS ensures the accuracy of its observations through a high-precision solar calibration method. Using the abundant simultaneous Earth observation data gathered by the SCS, this study realizes the radiometric calibration of each sensor, and the radiometric degradation model is further constructed to adjust the radiometric level and track and correct the sensor performance trend. The Chinese Ocean Color and Temperature Scanner (COCTS) is taken as an example, and the results show that the annual average radiometric degradation rate of COCTS-HY1C after launch ranges from 0.8% to 2.4% yr(-1), while that of COCTS-HY1D ranges from 0.5% to 3.9% yr(-1). These degradations were achieved by the SCS that allowed the sensors onboard HY1C/D to perform in-orbit radiometric calibration independently of external data sources and continuously generate high-quality climate data records (CDRs).

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.

The extraction of pigment characteristic spectra from the phytoplankton absorption spectrum has high application value in phytoplankton identification and classification and in quantitative extraction of pigment concentrations. Derivative analysis, which has been widely used in this field, is easily interfered with by noisy signals and the selection of the derivative step, resulting in the loss and distortion of the pigment characteristic spectra. In this study, a method based on the one-dimensional discrete wavelet transform (DWT) was proposed to extract the pigment characteristic spectra of phytoplankton. DWT and derivative analysis were applied simultaneously to the phytoplankton absorption spectra of 6 phyla (Dinophyta, Bacillariophyta, Haptophyta, Chlorophyta, Cyanophyta, and Prochlorophyta) to verify the effectiveness of DWT in the extraction of pigment characteristic spectra.

Since the first report in 2008, macroalgal blooms of Ulva prolifera (often called green tides) in the Yellow Sea have occurred every year, with their origins, transport pathways, temporal changes, as well as causes and consequences studied extensively. Of these studies, satellite remote sensing has been used widely to detect the bloom presence and quantify the bloom size (i.e., U. prolifera coverage in km2 or biomass in kilotons). However, substantial variability has been found in the refereed literature in the remote sensing methodology, results, and interpretation of the U. prolifera coverage, especially in the attempts to study inter-annual changes or long-term trends. There are often inconsistent or contradicting results even from the same satellite sensor. Such inconsistencies or contradictions create difficulty not only within the remote sensing community when presenting new methodology or results, but also to researchers when attempting to use the remote sensing results to make predictions or perform impact assessments. Here, we review the literature on the remote sensing methodology to detect and quantify U. prolifera blooms, and make recommendations based on physical principles. Specifically, we propose the following conceptual guidelines: 1) a reliable index or algorithm should be relatively tolerant to perturbations by non-optimal observing conditions (thick aerosols, thin clouds, moderate sun glint, cloud-adjacent straylight, which can all be found frequently in the study region) for presence/absence detection, as well as to small errors in the selected thresholds to quantify U. prolifera; 2) a reliable index or algorithm should also make it relatively easy to account for variability in subpixel coverage of U. prolifera (i.e., through pixel unmixing) in order to obtain an accurate estimate of total U. prolifera coverage from an image; 3) a reliable data product (i.e., U. prolifera maps) should be able to account for the variable clouds when interpreting spatial patterns or temporal changes, with uncertainty estimates provided whenever possible; and 4) both the algorithm and the data product should minimize manual work in order to make them more objective and repeatable by other researchers. Finally, we show different types of time series of U. prolifera amounts in the Yellow Sea using the approaches based on these guidelines and Moderate Resolution Imaging Spectroradiometer (MODIS) observations, and discuss their implications on the interpretation of annual changes in interdisciplinary studies.

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The Chinese Ocean Color and Temperature Scanner（COCTS） onboard HY-1D satellite（COCTS HY-1D） was launched on June 11, 2020. However, the performance of COCTS HY-1D has not yet been completely evaluated. In this study, the performance of COCTS HY-1D was first evaluated by comparing satellite derived remote sensing reflectance(R rs ) with in situ measurements collected at four AERONET-OC sites and two Chinese long-term platforms.Initially, the in situ data at four AERONET-OC sites were acquired to evaluate the performance of COCTS HY-1D in the global coastal waters. AERONET-OC is an ocean color component of the AERONET and provides long-term high-quality in situ normalized water leaving radiance(L wn ) measured by an autonomous radiometer system on an offshore fixed platform to support the calibration and validation of satellite ocean color sensors in coastal waters. Muping and Dong’ou sites were constructed by the China National Satellite Ocean Administration Service（NSOAS）, and the data were processed following the same procedure as that of the AERONET-OC data processing scheme. The COCTS HY-1D Level 1B data covering AERONET-OC sites and two long-term platforms between 1 August 1 2020 and 31 January 31 2021 in cloud-free days were acquired from NSOAS and processed to Level 2 R rs and Chl-a concentration products. Furthermore,R rs and Chl-a concentration comparison with two well-calibrated ocean color sensors（i.e., MODIS Aqua and VIIRS-SNPP） were made to evaluate the performance of COCTS HY-1D on the global scale. Additionally, the COCTS HY-1D Level 1B daily global dataset between December 7 and 14, 2020 were also required from NSOAS, processed to Level 2, and binned to Level 3 daily and 8-day 9-km data products by using the spatial-temporal binning algorithms developed by NASA. MODIS Aqua and VIIRS-SNPP Level 3 global binned daily and 8-day 9-km R rs and Chl-a concentration data collected between December 7 and 14, 2020 were acquired from NASA GSFC. The statistics used in this study included correlation coefficient（r）, Root Mean Square Error（RMSE）, Mean Absolute Percentage Error（MAPE）, and mean bias（mBias）.Results demonstrated that COCTS HY-1D-derived R rs agreed well with the in situ data at all wavelengths with the correlation coefficient r of visible bands between 0.91 and 0.98 and up to 0.98 and Mean Absolute Percentage Error（MAPE） of 22.9%. The product’s accuracy is comparable to the average MAPE of 20.5% between MODIS Aqua and in situ data. At the global scale, the COCTS HY-1D-derived R rs and chlorophyll concentration were consistent with MODIS Aqua products with a mean correlation coefficient ranging from 0.84 and to 0.95. The correlation coefficient of Chl-a is 0.85,which is higher than 0.76 between MODIS Aqua and VIIRS-SNPP. Nevertheless, the satisfactory R rs was derived from COCTS HY-1D at the global scale compared with the in situ measurements or well-calibrated MODIS Aqua and VIIRS-SNPP products.COCTS HY-1D can provide high quality ocean color products comparable with the international mainstream ocean color satellite sensors, and therefore can carry out stable and accurate ocean color remote sensing observation.

Particle size distribution (PSD) is a fundamental property that affects almost every aspect of the marine ecosystem, including ecological trophic interactions and transport of organic matter and trace elements. We measured PSDs using a suite of seven instruments in waters near Ocean Station Papa in the Northeast Pacific Ocean. These instruments and their sizing ranges are: Laser In-Situ Scattering and Transmissometer (LISST)-Volume Scattering Function meter (VSF) and Multispectral Volume Scattering Meter (MVSM), both sizing particles from 0.02 µm to 2000 µm; the LISST-100X, from 3 µm to 180 µm; the ViewSizer, from 0.3 µm to 2 µm; the Coulter Counter, from 2 µm to 40 µm; the Imaging Flow CytoBot (IFCB), from 5 µm to 100 μm; and the underwater vision profiler (UVP), from 100 µm to 2000 µm. Together, they cover an unprecedented size range spanning 5 orders of magnitude from 20 nm to 2 mm. The differences in size definition for the different instruments cause biases in comparing PSDs. The absolute differences in PSDs, after correcting for mean biases, were less than a factor of 3 among all the instruments, and within 50% among LISST-100X, LISST+MVSM, Coulter Counter and IFCB. We also found that particles of sizes <50 µm were not very porous; however, porosity must be considered for particles >50 µm. The merged PSDs, ranging from 0.02 µm to 2000 µm, showed little variation in the PSD slope in the upper 75 m of the water column even though the total number of particles decreased with depth. While submicrometer particles are numerically dominant, particles of sizes 1 µm to 100 µm account for 70–90% of the solid volume of particles. We expect that the results of this study will lead to improved estimates of mass and carbon flux in the study area.

Red Noctiluca scintillans (RNS) is one of the major red tide species and dominant bioluminescent plankton in the global offshore. Bioluminescence offers a number of applications for ocean environment assessments such as interval waves study, fish stocks evaluation and underwater target detection making it of significant interest in forecasting bioluminescence occurrence and intensity. RNS is susceptible to changes in marine environmental factors. However, the effects of marine environmental factors on the bioluminescent intensity (BLI, photon s-1) of individual RNS cells (IRNSC) is poorly known. In this study, the effects of temperature, salinity and nutrients on the BLI were studied by field and laboratory culture experiments. In the field experiments, bulk BLI was measured by an underwater bioluminescence assessment tool at various temperature, salinity and nutrient concentrations. To exclude the contribution by other bioluminescent planktons, an identification method of IRNSC was first developed using the features of the bioluminescence flash kinetics (BFK) curve of RNS to identify and extract BLI emitted by an individual RNS cell. To decouple the effects of each environmental factor, laboratory culture experiments were conducted to examine the effects of a single factor on the BLI of IRNSC. The field experiments showed that BLI of IRNSC negatively correlated with temperature (3-27°C) and salinity (30-35‰). The logarithmic BLI can be well fitted using a linear equation with temperature or salinity with Pearson correlation coefficients of -0.95 and -0.80, respectively. The fitting function with salinity was verified by the laboratory culture experiment. On the other hand, no significant correlation was observed between BLI of IRNSC and nutrients. These relationships could be used in the RNS bioluminescence prediction model to improve the prediction accuracy of bioluminescent intensity and spatial distribution.

Accurate Noctiluca scintillans bloom (NSB) recognition from space is of great significance for marine ecological monitoring and underwater target detection. However, most existing NSB recognition models require expert visual interpretation or manual adjustment of model thresholds, which limits model application in operational NSB monitoring. To address these problems, we developed a Noctiluca scintillans Bloom Recognition Network (NSBRNet) incorporating an Inception Conv Block (ICB) and a Swin Attention Block (SAB) based on the latest deep learning technology, where ICB uses convolution to extract channel and local detail features, and SAB uses self-attention to extract global spatial features. The model was applied to Coastal Zone Imager (CZI) data onboard Chinese ocean color satellites (HY1C/D). The results show that NSBRNet can automatically identify NSB using CZI data. Compared with other common semantic segmentation models, NSBRNet showed better performance with a precision of 92.22%, recall of 88.20%, F1-score of 90.10%, and IOU of 82.18%.

Satellite calibration spectrometer (SCS) onboard HY-1C/HY-1D could support a direct calibration using the simultaneous nadir overpass (SNO) approach for imaging spectroradiometers with various band configurations on the same or different satellite platforms by providing precise hyperspectral radiance after onboard calibration employing a solar diffuser (SD). However, a unique phenomenon was discovered in the analysis of annual variations of onboard calibration coefficients of the SCS. Although the solar calibration scheme employing an SD was used, a season-dependent oscillation still existed, and the oscillation trend of calibration coefficients is highly correlated with the variation trend of solar beta angle. In this article, we analyzed the entire solar calibration results of SCS from HY-1C using more than three years of data and discovered that season-dependent oscillation is primarily due to the variations in the transmittance of the solar attenuation screen affected by satellite platform attitude. Furthermore, an accurate variation model of the incident angle on the solar attenuation screen was developed, and the findings demonstrate that the season-dependent oscillation of onboard calibration coefficients of SCS could be effectively removed. Finally, the model established in this article is validated by optical simulation, which shows its consistency and reliability.

Large-scale green tides occurring in the Yellow Sea(YS)of China have become a critical eco-environmental problem,causing serious damage to marine and the coastal ecological environment,aquaculture,and tourism since 2007.Green tide biomass is a key parameter for accurate quantification of floating macroalgae,serving as an effective indicator for monitoring changes in the marine ecological environment.Satellite remote sensing technology plays a pivotal role in supporting the monitoring and assessment of green tide.Spaceborne optical sensors,in particular,offer a wealth of data that is indispensable for the fine-scale quantitative monitoring and assessment of green tide.In this study,we have established robust statistical relationships between Biomass Per Area(BPA)and various optical remote sensing indices by modeling the laboratory measurements of U.prolifera biomass(wet weight)per unit area and the corresponding spectral reflectance data.The computational methods of BPA have been carefully designed and validated for different optical data,including Moderate Resolution Imaging Spectroradiometer(MODIS),the Multispectral Instrument(MSI)onboard Sentinel-2 satellites,and the Coastal Zone Imager(CZI)onboard China's HaiYang-1C/D(HY-1C/D)satellites.These results indicate that BPA can serve as a highly effective parameter in quantifying green tide using remote sensing data.Unlike common parameters such as pixel area or coverage area,BPA can mitigate the scale effects of spatial resolution differences from various observations,minimizing the uncertainty especially when integrating multiple remote sensing data.With the coordinated utilization of CZI and MODIS data in 2021 and the developed BPA models,the detailed intra-annual variations in green tide biomass in the YS of China were quantified.This analysis has revealed the intricate spatial distribution patterns and trends inherent in green tide biomass fluctuations.The utilization of multiple optical remote sensing data sources for the estimation of green tide biomass carries important methodological significance and serves as an accurate data reference for the precise,quantitative,and dynamic monitoring of green tide in the YS of China.