Secchi disk depth (Z_SD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for Z_SD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (R_rs, sr⁻¹) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the R_rs (656) (R_rs at 656 nm) in the whole lake is less than 0.0015 sr⁻¹; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 Z_SD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the Z_SD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.

Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R² = 0.71, MAPE = 15.78%) than that for the model without turbidity (R² = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).

Aquatic vegetation is an important component and main primary producer of lake ecosystems and plays an important role in improving water quality and maintaining biodiversity, which is critical to diagnosing the health of aquatic ecosystems in shallow lakes. It is therefore important to accurately obtain information on dynamic changes and spatial-temporal distribution of aquatic vegetation. Based on the Sentinel-2 satellite remote sensing images from 2016–2022, we studied the feasibility of using remote sensing technology to monitor the spatial-temporal changes of aquatic vegetation before and after the removal of the fence, taking Futou Lake in Hubei Province as a case study. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Submerged Aquatic Vegetation Index (SAVI) were applied to identify the open water and the aquatic vegetation through two threshold determination methods, Otsu algorithm and manual division threshold method. The results show that: (1) the classification based on the NDVI and manual division threshold method performs the best, with the overall classification accuracy of 94.44% and the Kappa coefficient of 85.23%. (2) The growth of aquatic vegetation is divided into stages, the first stage is enclosing culture, and the distribution of aquatic vegetation is less in 2016–2017, all around 10 km². The second stage is after the removal of the fence, the distribution area of aquatic vegetation in 2018 is on an upward trend, and in 2019–2022 it is growing rapidly. (3) Spatially, the aquatic vegetation was mainly distributed at the former fence, specifically in the northeastern and southwestern waters of the Futou Lake and it spread to the core area of the lake, probably due to the elevation of the siltation of the lake bottom. (4) Potamogeton crispus and Trapa are the dominant species, the peak of the distribution range in Futou Lake occurs in 2021 with an area of about 50.89 km², which needs to be controlled moderately. (5) The area covered by Potamogeton crispus in the Futou Lake has increased significantly, probably due to the siltation and accumulation of nutrients in the Futou Lake caused by the history of purse seine farming.

Estimation of primary production in Qinghai Lake is crucial for the aquatic ecosystem management in the northeastern Qinghai–Tibet Plateau. This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m²/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes.

Quality water plays a huge role in human life. Chlorophyll-a (Chl-a) in water bodies is a direct reflection of the population size of the primary productivity of various phytoplankton species in the water body and can provide critical information on the health of water ecosystems and the pollution status of water quality. Case 2 Regional CoastColour (C2RCC) is a networked atmospheric correction processor introduced by the Sentinel Application Platform for various remote sensing products. Among them, the Extreme Case-2 Waters (C2X) process has demonstrated advantages in inland complex waters, enabling the generation of band data, conc_chl product for Chl-a, and kd_z90max product for Secchi Depth (SD). Accurate in situ data are essential for the development of reliable Chl-a models, while in situ data measurement is limited by many factors. To explore and improve the uncertainties involved, we combined the C2X method with Sentinel-2 imagery and water quality data, taking lakes in Wuhan from 2018 to 2021 as a case. A Chl-a model was developed and validated using an empirical SD model and a neural network incorporating Trophic Level Index (TLI) to derive the predicted correction result, Chl-a_t. The results indicated that (1) the conc_chl product measured by C2X and in situ Chl-a exhibited consistent overall trends, with the highest correlation observed in the range of 2–10 μg/L. (2) The corrected Chl-a_t using the conc_chl product had a mean absolute error of approximately 10–15 μg/L and a root-mean-square error of approximately 8–10 μg/L, while using in situ Chl-a had a root-mean-square error (RMSE) of approximately 15 μg/L and a mean absolute error (MAE) of approximately 20 μg/L; both errors decreased by double after correction. (3) The correlation coefficient (R) between Chl-a_t and each data point in the Chl-a model results was lower than that of SD-a_t with each data point in the SD model results. Additionally, the difference in R-value between Chl-a_t and each data point (0.45–0.60) was larger than that of SD-a_t with each data point (0.35–0.5). (4) When using corrected Chl-a_t data to calculate the TLI estimation model, both RMSE and MAE decreased, which were 1μg/L lower than those derived from uncorrected data, while R increased, indicating an improvement in accuracy and reliability. These findings demonstrated the presence of in situ errors in Chl-a measurements, which must be acknowledged during research. This study holds practical significance as some of these errors can be effectively corrected through the use of C2X atmospheric correction on spectral bands.

China’s coastal fisheries face challenges to their sustainability due to climate and human-induced pressures on key habitat drivers. This study provides an 18-year (2003–2020) assessment of six key ecological and data-available environmental factors (sea-surface temperature (SST), salinity, transparency, currents (eastward velocity, EV; northward velocity, NV), and net primary productivity (NPP), selected for their ecological relevance and data availability, across the Bohai, Yellow, and East China Seas at a spatial resolution of 0.083°. Non-parametric trend tests and seasonal climatologies were applied using MODIS-Aqua and CMEMS data with a refined quasi-analytical algorithm (QAA-v6). The results show distinct gradients: SST ranging from 9 to 13 °C (Bohai Sea) to >20 °C (East China Sea); transparency ranging from <5 m (turbid coasts) to 29.20 m (offshore). Seasonal peaks occurred for SST (summer: 18.92 °C), transparency (summer: 12.54 m), and primary productivity (spring: 1289 mg/m²). Long-term trends reveal regional SST warming in the northern Yellow Sea (9.78% of the area), but cooling in the central East China Sea. Widespread increases in transparency were observed (65.14% of the area), though productivity declined significantly (27.3%). The drivers showed spatial coupling (e.g., SST–salinity r = 0.95), but the long-term trends were decoupled. This study provides a comprehensive and long-term assessment of multiple key habitat drivers across China’s coastal seas. The results provide an unprecedented empirical baseline and dynamic management tools for China’s changing coastal ecosystems.