This document discusses using remote sensing to analyze spatial and temporal patterns in lake water quality on a global scale. It introduces the GloboLakes project which aims to identify coherence patterns in characteristics like temperature, chlorophyll, and total suspended matter in 1000 lakes using large satellite data streams. Functional data analysis methods are developed to cluster lakes based on similarities in their smoothed time series patterns while addressing challenges like missing data and ice cover. A example uses these methods to find 11 coherent groups of 732 global lakes based on their bi-monthly temperature patterns over 17 years.