Future GEO AquaWatch Webinars
January 14, 2021 2pm UTC
Title: Towards Macroscale Application of Inland Water Remote Sensing by Simon Topp, University of North Carolina – Chapel Hill
Remote sensing has the potential to vastly improve our ability to observe and monitor freshwater resources. While work on the subject dates back more than fifty years, it’s only recently that publications have moved from focusing on algorithm development to the implementation of those algorithms to address challenging science and management questions at large spatiotemporal scales. In this talk, we will explore examples of this macroscale research, including examinations of global river ice cover, national analyses of U.S. lake water clarity and seasonality, and patterns of basin-wide suspended sediment for over 100,000 km of U.S. rivers. Accompanying these examples, we will explore the resources that make applying remote sensing to macroscale questions more accessible than ever. AquaSat, along with similar databases like LIMNADES, provides hundreds of thousands of coincident satellite and field observations to develop and test models for key water quality parameters like chlorophyll-a, suspended sediments, and colored dissolved organic matter. These databases are supplemented by new resources like LimnoSat-US, which contains over 22 million remote sensing observations of U.S. lakes dating back to 1984. Such datasets reduce computational and technical barriers for non-remote sensing experts to incorporate remote sensing into their work. While challenges to implementing remote sensing as a tool for macroscale freshwater research still exist, we are currently in the middle of a paradigm shift moving away from localized analyses towards generalizable models, real-time spatially explicit monitoring, and increased understanding of the complex global dynamics of our freshwater systems.
Simon Topp is a PhD candidate completing his degree at the University of North Carolina at Chapel Hill. His research focuses on applying data science and machine learning approaches to large-scale analyses of water resources. This work includes examining long-term patterns in U.S. lake clarity and lake phenology, developing freshwater remote sensing datasets, and examining anthropogenic impacts on freshwater landscapes across the world. Through this work, Simon tries to make remote sensing more accessible to limnologists and ecologists by reducing technical barriers to incorporating satellite observations into research. Simon has led and/or contributed to the development of datasets such as LimnoSat-US and RiverSR, which contain over 35 years of remote sensing observations for all U.S. lakes and rivers respectively, as well as AquaSat, a large labelled U.S. database of coincident field and satellite observations of key water quality parameters. Simon received his Bachelor’s and Master’s degrees from Bard College in New York where he focused on the impacts of landuse change to water quality.