Maximizing Multi‐Decadal Water Surface Elevation Estimates With Landsat Imagery and Elevation/Bathymetry Datasets

Abstract Identifying patterns and trends in long‐term lake dynamics is essential to establish effective water management procedures and boost our understanding of inland water's role in the global water cycle. This research leverages Google Earth Engine to estimate multi‐decadal water surface elevations for 52 lakes and reservoirs with varying physical properties. Water surface elevation was estimated using the entire Landsat 4, 5, 7, and 8 Landsat Top‐of‐Atmosphere Tier‐1 Collection‐1 archive from August 1982 through December 2017 via shoreline boundary statistics extracted from the National Elevation Dataset merged with lake bathymetry. Image contamination was identified and removed to provide elevation estimates for images with varying levels of image contamination. To improve accuracy, data filtering techniques were identified which retained over 70% of images with detectable water boundaries producing 26 lakes with sub‐meter root‐mean‐square‐error accuracy and 40 lakes with sub‐meter mean‐absolute‐error‐accuracy using a general overall parameter model. Additionally, lake‐specific locally optimized models were also determined with 45 of the 52 lakes producing sub‐meter root‐mean‐square‐error accuracies and 49 with sub‐meter mean‐absolute‐errors with individual lake accuracy as low as 0.191 m RMSE CI95%[0.129, 0.243]. In general, individual lake accuracy is highly correlated with the mean slope of the surrounding terrain with low‐slope shorelines having greater accuracy than high‐slope shorelines. Seasonal patterns in estimate accuracy were also identified. This research extends our ability to track lake dynamics over long time periods to lakes lacking traditional in‐situ monitoring, enables rapid assessment of lake dynamics across large areas, and balances a need for both high‐accuracy measurements and maximum temporal resolution.