Effective monitoring of lake aquatic environments is crucial for assessing lake health, identifying issues, and developing emergency plans. Satellite-based remote sensing has been recognized as an effective method for timely and comprehensive monitoring of these environments. However, satellite-derived products often lack complete spatial coverage due to invalid pixels resulting from factors such as cloud cover, high sun glint contamination, and high satellite-viewing angles. To address this issue, we propose a novel gap filling method for satellite-derived products of lake aquatic environments, utilizing historical big data. We initially developed a machine-learning-based model for similarity matching across various dates. This model was based on 10 factors, selected from water quality and meteorological conditions that have a significant correlation with the lake aquatic environment. This model allows for the assignment of values to invalid pixels in a specific satellite-derived product, derived from the corresponding pixels in the products of historical dates. The proposed method has been applied to the satellite-derived Chl-a products of Lake Chaohu. The experimental findings demonstrate that the computed mean value of the peak signal-to-noise ratio (PSNR) stands at 35.75, as derived from the experimental data. This substantiates the precision of the gap filling method applied to satellite-derived products. This study underscores the significant value of the proposed method in gap filling for satellite-derived products, as well as in predicting the aquatic environment of lakes.