The remote sensing inversion of riverine algal blooms is frequently compromised by boundary effects from riparian wetlands, and the accuracy of traditional methods remains limited in narrow, elongated water bodies. As a typical sensitive water area, the terminal reach of the Ganjiang River still lacks a clear understanding of its algal bloom outbreak mechanisms. Utilizing Sentinel-2 and Landsat series satellite data, this study developed an improved method that integrates inward-masking technology with the floating algae index (FAI) and Otsu’s threshold to effectively suppress nearshore interference and accurately extract bloom extents in elongated rivers. Applying this method, we reconstructed the algal bloom outbreak processes in the Ganjiang River from 2019 to 2024. The results indicated that blooms exhibited pronounced seasonality, occurringprimarily in late summer and early autumn (August-September). Spatially, they were significantly aggregated in nearshore slow-flowing zones along the windward banks of the southern and middle branches, with intensity gradually decreasing from the littoral zone toward the thalweg. Driver analysis using a Random Forest regression model revealed that daily maximum temperature was extremely significantly correlated with bloom area, contributing 47.1%—far exceeding the contributions of nutrients (29.9%), mean daily wind speed (9.3%), mean daily discharge (7.3%), and daily rainfall (6.4%). Furthermore, by analyzing bloom dynamics and environmental conditions during typical heatwave events (≥35 ℃ for three consecutive days), this study demonstrates that under meso-eutrophic conditions, low discharge and water retention during summer and autumn form the basis for bloom accumulation, while extreme heatwaves act as dominant drivers. Their coupling with low discharge and nutrient availability significantly amplifies the scale of bloom outbreaks. This study provides valuable insights into the mechanisms governing riverine algal bloom outbreaks.