Floating-leaved and emergent aquatic vegetation play crucial roles as primary producers in lake ecosystems, each fulfilling distinct ecological functions. Monitoring the spatial distribution and changes of floating-leaved and emergent aquatic vegetation using satellite remote sensing is essential for lake ecological assessment and carbon source-sink accounting. However, distinguishing between the two types of aquatic vegetation using only optical remote sensing data is challenging due to their typical spectral characteristics. This challenge is further compounded by algal blooms in eutrophic lakes, which also exhibit similar spectral characteristics. To address this issue, we proposed an automatic classification algorithm for identifying two types of aquatic vegetation by combining Sentinel-1 SAR and Sentinel-2 MSI data. Firstly, we identified areas with vegetation spectral characteristics using the Normalized Difference Vegetation Index (NDVI) and Otsu’s method in lakes. Then, in these regions, the first principal component (PCA1) of Sentinel-1 SAR image and the K-means clustering algorithm were used to extract floating-leaved and emergent aquatic vegetation. Its noted that PCA1 was a key classification indicator of the algorithm, which could remove the interference of algal blooms and achieve the separation of floating-leaved and emergent aquatic vegetation. The algorithm was conducted accuracy validation in four typical lakes (i.e. Lake Taihu, Lake Ulansuhai, Lake Yangcheng and Lake Nanyi), with an average overall classification accuracy of 83.76% and a Kappa coefficient of 0.71. Based on the algorithm, we mapped and analyzed the intra-annual and inter-annual variations of floating-leaved and emergent aquatic vegetation in Lake Taihu. The results showed that the area of both groups reached their coverage peaks from July to October. From 2016 to 2023, the area of floating-leaved aquatic vegetation significantly increased from 24.21 km2 to 68.03 km², while the area of emergent aquatic vegetation remained relatively stable and the average annual area was 41.48 km². This algorithm not only addresses the difficulties in identifying floating-leaved aquatic vegetation and emergent aquatic vegetation, but also achieves automation. It has great potential for monitoring large-scale spatial and temporal changes of floating-leaved aquatic vegetation and emergent aquatic vegetation in lakes. This provides the technical support for future lake ecological assessments and carbon source-sink accounting.