Suspended particulate matter (SPM) is a key optically active constituent in lake water. Its concentration governs turbidity, water colour and transparency, and therefore serves as a critical indicator of lake water quality. Taihu, Hongze and Chao—the three largest lakes in the middle–lower Yangtze River basin—exhibit pronounced spatio-temporal SPM variabilitydriven by both natural processes and human activities. Although polar-orbiting satellites have been widely used to characterize seasonal-to-interannual SPM dynamics, the scarcity of cloud-free images severely limits analyses at monthly or daily resolution.Here, 132 620 Level-2 images from the Geostationary Ocean Color Imager (GOCI) acquired between April 2011 and December 2020, together with quasi-synchronous in-situ data collected over the three lakes, were used to evaluate existing SPM algorithms. After selection and optimisation, the best-performing algorithm was applied to the quality-controlled GOCI imagery to generate a long-term hourly SPM concentration dataset.The dataset is provided in GeoTIFF format under the geographic coordinate system GCS_WGS_1984 and consists of 13 262 files. On average, the number of valid observational days per month (defined as the fraction of usable pixels > 85 %) is 9.1 for Taihu, 7.6 for Hongze and 7.5 for Chao. Cross-validation shows excellent agreement between satellite retrievals and field measurements in both spatial patterns and long-term temporal trends (R2 > 0.90, MAPE < 15 %, RMSE < 5 mg L?1), demonstrating the robustness and reliability of the product.Compared with polar-orbiting satellite products, the present dataset offers an order-of-magnitude improvement in temporal resolution, delivering complete SPM dynamics at daily and monthly scales and accurately capturing seasonal and inter-annual variations. The dense time series further enables high-frequency SPM monitoring, allowing short-term, high-magnitude disturbances—such as rainstorm runoff, dredging and water-diversion events—to be resolved. The dataset thus provides essential support for elucidating the short- and long-term mechanisms underlying SPM spatio-temporal variability and is of great value for lake management and scientific research.