Supporting Shellfish Aquaculture in the Chesapeake Bay using Artificial Intelligence to Detect Poor Water Quality through Sampling and Remote Sensing

Aquaculture in the Chesapeake Bay and around the world is the fastest growing food-producing sector, now accounting for almost 50% of the world's fish harvest according to the FAO. Yet waterways face increasing pressures as the world's population grows near the coasts and extreme weather leads to greater run-off from land. Pollutants such as agricultural fertilizers, livestock waste, and overflowing sewage make their way into streams and rivers, with detrimental impacts on water quality in the Bay and elsewhere [e.g. 2019 Chesapeake Bay Foundation Report Card; MD DNR, 2019; Schollaert Uz et al., 2019]. Responsible management of aquaculture requires access to reliable information on a variety of environmental factors that are not currently available at optimal scales in space and time. To address this challenge, we propose to expand the use of Earth observations from satellites and other data sets by using an Analytic Center Framework (ACF) Artificial Intelligence (AI) tool to improve efficiencies in managing diverse environmental datasets. Computationally-intensive AI algorithms trained with many sources of observations have potential to detect patterns of poor water quality not previously possible through traditional techniques. Our team of experts in remote sensing, aquatic ecology and biogeochemistry, along with Maryland Department of the Environment (MDE) shellfish regulators, has been conducting a scoping study of this problem since last fall in search of optical signatures in the water around leaking septic systems. We have been collecting and analyzing biological, chemical, and physical variables in and above the water at target sites and in the lab. Although toxins that typically cause shellfish bed closures are not discernable by traditional multispectral techniques, we are looking for hyperspectral proxies covarying with such toxins. One promising result of our pilot study has been the detection of a shifted fluorescence emission from dissolved organic matter along with emission by phycoerythrin pigments associated with high fecal coliform counts. Now we propose to team up with computer scientists to apply an AI model to this problem, using datasets collected monthly at 800 sites around the Bay for decades, in combination with remotely sensed satellite and aerial data during targeted field work to support an applied research need to more effectively and rapidly sort through disparate data sets to identify areas of poor water quality resulting in shellfish bed closure. While this project is a proof-of-concept in the well-sampled Chesapeake Bay, the long-term goal is for a global application on a satellite platform. This is aligned with several goals of NASA for this decade, namely through the development of applications that contribute to managing water quality, one of the essential but overlooked elements of regional and local sustainable water resources management. This proposal is relevant to the AIST Program Element by integrating previously unlinked datasets and tools into a common platform to address this previously intractable science problem.

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