Zheng Duan

Shallow lakes worldwide face escalating pressures from eutrophication and climate change, yet comprehensive monitoring of Chlorophyll-a (Chl-a) spatiotemporal dynamics remains challenging due to the high costs and logistical constraints of traditional sampling approaches across large, heterogeneous water bodies. Lake Balaton, a large shallow lake system (80 km long, 7 km wide, 3.7 m mean depth) in Central Europe, exemplifies these monitoring challenges while serving as a representative system for understanding climate-driven changes in temperate shallow lakes. Despite decades of in-situ measurements along the lake's centerline, fine-scale spatial patterns and long-term temporal trends in Chl-a remain poorly characterized due to sparse samplings. Using a machine-learning-derived optical remote sensing dataset (1984–2023) at 30 m spatial resolution, we conducted comprehensive spatiotemporal analysis of Chl-a dynamics and examined relationships with bathymetry, nutrient loading, and light availability features. Our analysis reveals an exponential west-to-east decline in Chl-a concentrations with distance from the primary nutrient source, characterized by a consistent decay rate of 0.04–0.06 km^-1 (typically 0.05 km^-1). Littoral zones consistently exhibited 1.3–2.8 times higher optical Chl-a concentrations than pelagic zones, reflecting integrated signals from phytoplankton, benthic algae, and submerged macrophytes. Phenological analysis demonstrated significant climate-driven shifts, with peak Chl-a timing advancing by 20 days over the study period and growing season onset occurring 10 days earlier, consistent with regional warming trends. These findings provide a transferable framework for satellite-based water quality monitoring in shallow lake systems and demonstrate the critical importance of accounting for spatial heterogeneity and climate-driven temporal shifts in lake management strategies globally.