Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, R_rs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m⁻³. Different empirical and semi-analytical approaches developed to assess SPM from R_rs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was 40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

Sea-surface chlorophyll-a concentration (Chl-a) is a core indicator reflecting phytoplankton biomass and marine ecological conditions. Its spatiotemporal variation patterns are closely related to environmental changes and human activities, especially in coastal waters around heavily populated areas, e.g., the Bohai Sea in China. Benefiting from long time-series ocean-color (i.e., Chl-a provided by Aqua-MODIS) multi-source merged sea surface temperature (SST) and wind speed (i.e., ERA5) and dissolved inorganic nitrogen concentration (DIN) data, this study investigated the long-term variation characteristics of Chl-a in the Bohai Sea and its influencing factors during the period of 2003 to 2022. After rigorous quality control and data reconstruction, this study analyzed the interannual, seasonal, and spatial variation patterns of Chl-a in the Bohai Sea across five ecological functional subregions (Bohai Bay, the Qinhuangdao coast, Liaodong Bay, Laizhou Bay, and the central Bohai Sea), and explored the influence of SST, wind speed, and DIN on variations in Chl-a. The results showed that the spatial distribution of Chl-a in the Bohai Sea exhibited a significant coastal–offshore gradient, with higher concentrations in coastal bays and the Qinhuangdao coast and lower concentrations in the central Bohai Sea. Temporally, despite a long-term trend of first increasing and then decreasing with a peak around 2011, Chl-a underwent a significant regime shift around 2015. After the shift, the average concentration decreased by 0.36 mg/m³ compared with that before the shift. On a seasonal scale, the average Chl-a concentration over the whole Bohai showed the largest decrease in summer (−0.65 mg/m³) and the smallest decrease in winter (−0.21 mg/m³), with contrasting changes among subregions: the Qinhuangdao coast had the most significant decrease (−1.54 mg/m³), while Laizhou Bay remained basically stable. Driver mechanism analysis indicated that Chl-a in the Bohai Sea was significantly negatively correlated with SST (r = −0.51, p = 0.022) and significantly negatively correlated with wind speed (r = −0.77, p < 0.01). Furthermore, both SST and wind speed have undergone significant regime shifts toward a warmer and a windier state, respectively. The timing of these climatic shifts coincided with or preceded the Chl-a regime shift, which may help suppress phytoplankton blooms and maintain lower Chl-a levels. In addition, the surface DIN concentration in Bohai Bay decreased by 23.6% after the Chl-a regime shift, indicating a reduction in nutrient input may be responsible for the decrease in Chl-a in this region. The research results reveal the long-term variation patterns and multi-factor synergistic regulatory mechanism of Chl-a in the Bohai Sea, providing a scientific reference for red-tide monitoring and early warning as well as regional ecological environment management in the Bohai Sea.

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters.

Satellite remote sensing observations and on-site observations on offshore platforms are currently the two most mainstream means of observing characteristics of marine aerosols. Among them, satellite remote sensing observations have the advantage of a wide range of observations, while on-site observations on offshore platforms can obtain observation data with higher time resolution, and have the advantages of easy calibration and maintenance, and high quality observation data. With the successive launch of my country’s operational marine satellites HY-1C and HY-1D, and the commissioning of the China Marine Satellite Maritime Calibration and Inspection Field, my country now has the ability to use marine optical satellites and on-site observation platforms in the area where the marine observation platform is located. Conditions for joint observation of aerosol characteristic parameters. In this paper, aiming at the offshore waters of Zhejiang where the Dongou Ocean Comprehensive Observation Platform is located, combining satellite observation data and field platform observation aerosol optical thickness, Angstrom index, scale distribution and fine-mode ratio, single scattering albedo, complex refractive index and other data, The analysis of the characteristics of aerosols in the coastal waters of Zhejiang shows that the aerosol properties in this area are stable and change little during the interannual period. The aerosol optical thickness is higher in spring and lower in autumn. In summer, the proportion of fine-mode aerosols is relatively high, the scattering effect is weak, and the absorption effect is relatively strong; while in winter, the coarse mode aerosols in the sea area occupy a larger proportion, the scattering effect is strong, and the relative absorption effect is weak. And the quantitative relationship between the single scattering albedo and the absorption coefficient of the aerosol scale distribution model of the sea area is given.