Frequent Sargassum beaching in the Caribbean Sea and other regions has caused severe problems for local environments and economies. Although coarse-resolution satellite instruments can provide large-scale Sargassum distributions, their use is problematic in nearshore waters that are directly relevant to local communities. Finer resolution instruments, such as the multispectral instruments (MSIs) on the Sentinel-2 satellites, show potential to fill this gap, yet automatic Sargassum extraction is difficult due to compounding factors. In this article, a new approach is developed to extract Sargassum features automatically from MSI Floating Algae Index (FAI) images. Because of the high spatial resolution, limited signal-to-noise ratio (SNR), and staggered instrument internal configuration, there are many nonalgae bright targets (including cloud artifacts and wave-induced glints) causing enhanced near-infrared reflectance and elevated FAI values. Based on the spatial patterns of these image “noises,” a Trainable Nonlinear Reaction Diffusion (TNRD) denoising model is trained to estimate and remove such noise. The model shows excellent performance when tested over realistic noise patterns derived from MSI measurements. After removing such noise and masking clouds (as well as cloud shadows and glint patterns), biomass density from each valid pixel is quantified using the FAI-biomass model established from earlier field measurements, from which Sargassum morphology (length/width/biomass) is derived. Overall, the proposed approach achieves over 86% Sargassum extraction accuracy and shows preliminary success on Landsat-8 images. The approach is expected to be incorporated in the existing near real-time Sargassum Watch System for both Landsat-8 and Sentinel-2 observations to monitor Sargassum over nearshore waters.