POSTER Session 3

TOWARDS LAKESENSE: A PROCESSING PIPELINE TO FACILITATE ACCESS TO TIME SERIES OF REMOTELY SENSED WATER QUALITY INDICATORS FOR US LAKES

Time series of water-quality indicators in lakes is crucial for understanding the impacts of climate change, pollution, and environmental policy and regulations. However, the vast majority of U.S. lakes remain unmonitored, with access to long-term and temporally dense observations of lake water quality indicators (LWQI) being limited to lakes with well-established monitoring programs. High-resolution optical remote sensing offers a promising solution to derive time series of LWQI for the 92,380 lakes ≥5 hectares distributed across the contiguous United States. However, easy access to reliable time series of remotely sensed LWQI is currently lacking, largely due to the inherent challenge of generating reliable LWQI in inland waters and the lack of a unified protocol and processing pipeline. Here, we present progress on the development of LakeSense, an automated processing pipeline to derive time series of LWQI from high-resolution remote sensing for lakes ≥5 hectares in the contiguous U.S. Developed in open-source software and deployable on Linux-based scientific computing clusters, LakeSense automates Sentinel-2/Landsat-8-9 image acquisition, atmospheric correction, and algorithm implementation to generate LWQI time series. Using a pilot sample of 102 lakes with dense in situ LWQI monitoring (by US EPA) and heterogeneous characteristics (eco-region, surrounding landscape, optical water types) we develop a set of heuristic rules for the selection of atmospheric corrections and algorithms to optimize the accuracy of LWQI retrievals (chlorophyll, turbidity). After this initial optimization, LakeSense will be implemented for all lakes in two pilot regions of the United States: 1) Minnesota-Wisconsin, and 2) Florida-Georgia-South Carolina.

*Sachini Ranasinghe, Boston University, USA, [email protected], https://orcid.org/0000-0002-1251-2981

Christoph Nolte, Boston University, USA, [email protected]

Cédric Fichot, Boston University, USA, [email protected]

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