Global climate change can greatly promote the continuing expansion of algal blooms in eutrophic inland lakes. Wind fields, an important climate factor, provide an external driving force for the movement of algal blooms. Based on algal bloom satellite imageries and wind observation data from 2003 to 2022, this study explored a quantitative assessment of the variations in surface wind fields and their impacts on the algal blooms in Lake Taihu, China. The results indicate that the mean wind speed at different time scales in the Lake Taihu area presents a continuous descending tendency in recent decades, which is the probable cause for the increasing frequency and severity of algal blooms in the lake. Wind fields affect the formation, location, and severity of algal blooms in diverse and complex ways. The area and frequency of algal blooms in Lake Taihu increase with the decrease in wind speed. The 6 h mean wind speed before 12:00 LT (Local Time) on the day of the algal bloom occurrence generally follows a Gaussian distribution, with a wind speed range of (0.6 m/s, 3.4 m/s) at the 95.5% confidence level. Accordingly, the wind speeds of 0.6 m/s and 3.4 m/s are identified to be the lower and upper critical wind speed indicators suitable for the formation of algal blooms, respectively. Another meaningful finding is that the outbreak of large-scale algal blooms requires stricter wind speed conditions, with a significantly lower wind speed threshold of around 2 m/s. Our study also demonstrates that the dominant wind direction of southeast in the region may be an important cause of the continuous water-quality decline and the high frequency and severity of algal blooms in the northwest waters of the lake. These findings will contribute to further studies on the dynamic mechanism of algal blooms and provide support for water environment management and algal bloom prevention and control.

China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.