Lakes are sentinels of climate change and human pressure, yet harmful algal blooms, loss of aquatic vegetation and water-quality deterioration are intensifying worldwide. Recent progress in satellite and UAV remote sensing, high-frequency sensor networks and artificial intelligence opens new opportunities for "smart" lake monitoring and operational early warning. This session invites contributions on: (1) retrieval algorithms for water-quality parameters (chlorophyll-a, cyanobacteria, turbidity, CDOM) and aquatic vegetation from multi-source remote sensing; (2) integration of in-situ, optical and physiological measurements for calibration and validation; (3) long-term (multi-decadal) reconstruction of lake ecosystem change; (4) data-driven and hybrid models (e.g. deep learning, KAN) for short-term forecasting of bloom events; and (5) cloud platforms (e.g. Google Earth Engine), open tools and management applications. We particularly welcome comparative studies across lakes and regions, as well as contributions from early-career scientists.