Driven by tidal forcing and terrestrial inputs, suspended particulate matter (SPM) in shallow coastal waters usually shows high-frequency dynamics. Although specific geostationary satellite ocean color sensors such as the geostationary ocean color imager (GOCI) can observe SPM hourly eight times in a day from morning to afternoon, it cannot cover the whole semi-diurnal tidal period (∼12 h), and an hourly frequency may be insufficient to witness rapid changes in SPM in highly dynamic coastal waters. In this study, taking the Yangtze River Estuary as an example, we examined the ability of the geostationary meteorological satellite sensor AHI/Himawari-8 to monitor tidal period SPM dynamics with 10-min frequency. Results showed that the normalized water-leaving radiance (Lwn) retrieved by the AHI was consistent with the in-situ data from both cruise- and tower-based measurements. Specifically, AHI-retrieved Lwn was consistent with the in-situ cruise values, with mean relative errors (MREs) of 19.58%, 16.43%, 18.74%, and 26.64% for the 460, 510, 640, and 860 nm bands, respectively, and determination coefficients (R²) larger than 0.89. Both AHI-retrieved and tower-measured Lwn also showed good agreement, with R² values larger than 0.75 and MERs of 14.38%, 12.42%, 18.16%, and 18.89% for 460, 510, 640, and 860 nm, respectively. Moreover, AHI-retrieved Lwn values were consistent with the GOCI hourly results in both magnitude and spatial distribution patterns, indicating that the AHI can monitor ocean color in coastal waters, despite not being a dedicated ocean color sensor. Compared to the 8 h of SPM observations by the GOCI, the AHI was able to monitor SPM dynamics for up to 12 h from early morning to late afternoon covering the whole semi-diurnal tidal period. In addition, the high-frequency 10-min monitoring by the AHI revealed the minute-level dynamics of SPM in the Yangtze River Estuary (with SPM variation amplitude found to double over 1 h), which were impossible to capture based on the hourly GOCI observations.

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

Photosynthetically available radiation (PAR) is essential for the photosynthesis processes of land plants and aquatic phytoplankton. Satellite observation with different diurnal frequencies (e.g., high frequency from geostationary satellites and low frequency from polar-orbit satellites) provides a unique technique to monitor PAR variation on large tempo-spatial scales. Owing to different climatic characteristics, different regions may require different observation frequencies to obtain accurate PAR estimation, but such requirements are still poorly known. Here, based on Advanced Himawari Imager (AHI) high-frequency (10-min) observation data from the geostationary satellite Himawari-8, we investigated the influence of diurnal observation frequency on the accuracy of PAR estimation and provided the minimal observing frequency to get high accurate PAR estimation in the AHI coverage area. Our results revealed a remarkable difference in the requirements for the diurnal observation frequency in both spatial and temporal distributions. Overall, high-latitude regions need a higher observing frequency than low-latitude areas, and winter half-years need higher observing frequency than summer half-years. These results provide a basis for designing satellites to accurately remote sensing of PAR in different regions.

The mesoscale eddy structure in the upper ocean is difficult to observe with altimeter satellite data due to their coarse spatiotemporal resolution. However, the world’s first Geostationary Ocean Color Imager (GOCI) makes it possible to refine the eddy structure inversion with observations of high spatiotemporal resolution (500 m and 1 h, respectively). In this study, we proposed a new flow field retrieval algorithm recurrent all-pairs field transforms in mesoscale eddy flow retrieval (RAFT-MesoEddy) based on deep current optical flow architecture in the mesoscale eddy surface flow field for GOCI data. The validation results showed that the flow field matched the particle image velocimetry (PIV) data in both quality and spatial distribution, with mean end-to-end point error (MEPE) and mean average angle error (MAAE) of less than 0.07 pixel and 2.57°, respectively. The GOCI-derived flow field showed good consistency with drifting buoy trajectory data, with MEPE and MAAE values less than 0.34 m/s and 18.78°, respectively. The GOCI-retrieved flow field showed agreement with satellite altimeter geostrophic flow data, with the MEPE and MAAE being 0.28 m/s and 10.12°, respectively. The GOCI-retrieved flow field showed an agreement with Ocean Surface Current Analyses Real-time (OSCAR) surface flow field, with the MEPE and MAAE are 0.28 m/s and 9.62°, respectively. In addition, a specific mesoscale eddy to discuss the hourly dynamics of flow field and eddy kinetic energy (EKE) is taken. The significant diurnal dynamics revealed in the hourly GOCI observations suggest caution in mapping mesoscale eddy flow using conventional altimeter satellite data, as the temporal resolution is insufficient to capture diurnal variations.

Diurnal monitoring of the Secchi-disk depth (SDD) of eutrophic lakes is the basic requirement to ensure domestic, industrial, and agricultural water use in surrounding cities. The retrieval of SDD in high frequency and longer observation period is the basic monitoring requirement to guarantee water environmental quality. Taking Lake Taihu as an example, the diurnal high-frequency observation (10 mins) data of the geostationary meteorological satellite sensor AHI/Himawari-8 were examined in this study. The results showed that the AHI normalized water-leaving radiance (L_wn) product derived by the Shortwave-infrared atmospheric correction (SWIR-AC) algorithm was consistent with the in situ data, with determination coefficient (R²) all larger than 0.86 and the mean absolute percentage deviation (MAPD) of 19.76%, 12.83%, 19.03% and 36.46% for the 460 nm, 510 nm, 640 nm and 860 nm bands, respectively. 510 nm and 640 nm bands showed more better consistency with in situ data in Lake Taihu. Therefore, an empirical SDD algorithm was established based on the AHI green (510 nm) and red (640 nm) bands. The SDD algorithm was verified by in situ data showed good performance with R² of 0.81, RMSE of 5.91 cm, and MAPD of 20.67%. Based on the AHI data and established algorithm, diurnal high-frequency variation of the SDD in the Lake Taihu was investigated and the environmental factor (wind speed, turbidity degree, and photosynthetically active radiance) corresponding to diurnal SDD variation were discussed. This study should be helpful for studying diurnal high-dynamics physical-biogeochemical processes in eutrophication lake waters.