Remote sensing monitoring of seasonal changes in phytoplankton density and analyses of the driving factors of phytoplankton densities are necessary for assessing the health of aquatic ecosystems, controlling lake eutrophication, and formulating ecological restoration policies. Building upon the satellite-ground synchronization experiment that involves the in situ aquatic ecological monitoring conducted in Nansi Lake, which is the largest storage lake situated along the eastern route of the South-to-North Water Diversion Project, we developed a phytoplankton density retrieval model utilizing the random forest (RF) method and Landsat-8 OLI data. On this basis, we mapped the seasonal fluctuations and spatial disparities in the phytoplankton densities from 2013 to 2023. Subsequently, we conducted a detailed analysis of the driving factors and considered both the natural and anthropogenic aspects. The results indicate that (1) the RF model, when utilizing three band combinations, yielded favorable results with R², RMSE and MAE values of 0.67, 1.31 × 10⁶ cells/L and 1.18 × 10⁶ cells/L, respectively. (2) The phytoplankton densities exhibited both seasonal and spatial variations, with higher concentrations in summer and autumn than in spring and winter. Significantly, the northwestern region of Zhaoyang Lake and the southeastern region of Weishan Lake had substantially greater phytoplankton densities than did the other areas. Furthermore, overarching upward trends were observed from 2013 to 2023, reflecting an annual rate of increase of 3.32%. (3) An analysis of the causal factors indicated that temperatures and gross agricultural production levels are the primary drivers influencing the seasonal variations and distributions of phytoplankton densities. In the future, we will delve into the potential of deep learning and utilize various satellite sensors to explore the intricacies of phytoplankton monitoring, as well as the complex mechanisms that influence aquatic ecological health.

遥感监测浮游植物密度季节变化，分析浮游植物密度驱动因素，对于评估水生生态系统健康状况、控制湖泊富营养化、制定生态修复政策至关重要。基于南水北调东线最大蓄水湖南四湖水生生态原位监测星地同步实验，建立了浮游植物密度反演模型。利用随机森林 (RF) 方法和 Landsat-8 OLI 数据。在此基础上，我们绘制了2013年至2023年浮游植物密度的季节波动和空间差异。随后，我们对驱动因素进行了详细分析，并考虑了自然和人为因素。结果表明：(1) RF 模型在使用三种波段组合时，产生了良好的结果，R ² 、RMSE 和 MAE 值分别为 0.67、1.31 × 10 ⁶ cells/L 和 1.18 × 10 ⁶ cells/L。(2)浮游植物密度呈现季节和空间变化,夏季和秋季浓度高于春季和冬季。值得注意的是，昭阳湖西北地区和微山湖东南地区的浮游植物密度明显高于其他地区。此外，从2013年到2023年，总体呈上升趋势，年增长率为3.32%。（3）影响因素分析表明，气温和农业总产水平是影响浮游植物密度季节变化和分布的主要驱动因素。未来，我们将深入挖掘深度学习的潜力，利用各种卫星传感器来探索浮游植物监测的复杂性，以及影响水生生态健康的复杂机制。