Freshwater cyanobacterial blooms pose a major threat to local ecosystems, economies, and public health. Monitoring these occurrences is essential for water resource managers worldwide. Satellite remote sensing techniques can detect and quantify blooms in large inland and estuarine water bodies but monitoring blooms in small water bodies (< 10 km(2)), including narrow river/canal systems, remains challenging. This is due to the coarse spatial resolution (>300 m) or low re-visit frequency (>10 days) of most operational satellite sensors. The ephemeral nature of cyanobacterial blooms and their tendency to form dense surface mats (or "scums") that aggregate nearshore further highlight the need for sensors with higher spatial and temporal resolutions. In this study, a deep learning model based on convolutional neural network U-net was developed to detect cyanobacterial blooms (i.e., scums) in the highly modified and managed Caloosahatchee River (i.e., C-43 canal) and Caloosahatchee River Estuary (CRE) (Florida, USA) using Dove imagery (3-m resolution) obtained near-daily from the PlanetScope satellite constellation. The approach consisted of three steps: 1) training and validating the U-net model with "ground truth" images; 2) classifying bloom pixels; and 3) quantifying bloom area using linear unmixing. Validation results indicate an overall F1 score of 89.6% when assessing bloom area. Application of the model revealed the westward expansion of a cyanobacteria bloom from C-43 to CRE in summer 2018, indicating the physical transport of the bloom originating upstream in Lake Okeechobee to the estuary. This approach was tested on other inland water bodies, indicating potential for monitoring cyanobacterial blooms on a global scale.