Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape-level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale.
Keywords: big data; ecology; lakes; limnology; phenology; remote Sensing.
© 2021. The Authors.