Submerged macrophyte monitoring is a major concern for hydrosystem management, particularly for understanding and preventing the potential impacts of global change on ecological functions and services. Macrophyte distribution assessments in rivers are still primarily realized using field monitoring or manual photo-interpretation of aerial images. Considering the lack of applications in fluvial environments, developing operational, low-cost and less time-consuming tools able to automatically map and monitor submerged macrophyte distribution is therefore crucial to support effective management programs. In this study, the suitability of very fine-scale resolution (50 cm) multispectral Pléiades satellite imagery to estimate submerged macrophyte cover, at the scale of a 1 km river section, was investigated. The performance of nonparametric regression methods (based on two reliable and well-known machine learning algorithms for remote sensing applications, Random Forest and Support Vector Regression) were compared for several spectral datasets, testing the relevance of 4 spectral bands (red, green, blue and near-infrared) and two vegetation indices (the Normalized Difference Vegetation Index, NDVI, and the Green-Red Vegetation Index, GRVI), and for several field sampling configurations. Both machine learning algorithms applied to a Pléiades image were able to reasonably well predict macrophyte cover in river ecosystems with promising performance metrics (R² above 0.7 and RMSE around 20%). The Random Forest algorithm combined to the 4 spectral bands from Pléiades image was the most efficient, particularly for extreme cover values (0% and 100%). Our study also demonstrated that a larger number of fine-scale field sampling entities clearly involved better cover predictions than a smaller number of larger sampling entities.