Integration of Observations and Models into Machine Learning for Coastal Water Quality

Coastal areas are impacted by population growth, development, aging infrastructure, and extreme weather events causing greater runoff from land. Monitoring water quality is an urgent societal need. A growing fleet of satellites at multiple resolutions provide the ability to monitor large coastal areas using big data analytics and machine learning. Within our AIST18 project, we started working closely with state agencies who manage water resources around the Chesapeake Bay. We propose to build upon these activities to improve the integration of assets to monitor water quality and ecosystem properties and how they change over time and space. Initially we are taking advantage of technologies and data collected in and around the Chesapeake Bay, with a plan to expand to other watersheds. As the largest estuary in North America, the Chesapeake Bay receives runoff from approximately 100,000 tributaries, carrying sediment, fertilizer, and pollutants from farms, developed communities, urban areas, and forests. These constituents degrade water quality and contribute to its optical complexity. Resource managers tasked with enforcing pollution reduction goals for these point and non-point sources are also challenged by shrinking budgets with which to monitor multiple aspects of the ecosystems while the use of the Bay for recreation, fishing, and aquaculture is increasing. Of particular concern are the increasing number of harmful algal blooms (HABs) and septic tank leaks due to aging infrastructure and rising sea level [Wolny et al., 2020; Mitchell et al., 2021]. State agencies already work closely with NOAA and EPA and are looking to NASA to apply advanced technologies to further improve their natural resource management. Our AIST18 project demonstrated promising results with multispectral optical, medium spatial resolution satellite data trained using geophysical model variables within a machine learning (ML) architecture by extracting multi-source feature maps. The nearshore environment demands finer spatial resolution than government assets alone can provide, thus we plan to build on this work by utilizing higher spatial resolution data from commercial satellites. Following our demonstration of feasibility using medium resolution satellite imagery from one sensor, we will now derive feature maps from many sensors of varying spatial, spectral, and temporal resolution. These can be effectively merged regardless of initial source resolution at progressively higher (hierarchical) contextual levels by fusing at multiple layers within the ML model. Heterogeneous feature maps can be adaptively scored and weighted, which influences their significance in the resulting predictions. We plan to analyze higher spectral information from in situ inherent optical property observations to determine the minimum set of requirements for remote sensing of water quality, e.g. water clarity, phytoplankton blooms, and the detection of pollutants. In situ observations will facilitate ML training using higher spectral and spatial resolution imagery from commercial satellites at the coast. We are also collaborating with community experts to evaluate the utility of hyperspectral remote sensing for detecting aquatic features not discernable through multispectral imaging, such as phytoplankton community structure and the likelihood of harmful blooms. In situ observations will facilitate ML training using hyperspectral and higher spatial resolution imagery from commercial satellites at the coastal margins and land-water interface. Finally, we aim to eventually integrate upstream assets of land cover classification, elevation, vertical land motion, and hydrology as inputs to the ML architecture, leveraging other projects that characterize the watershed and runoff of sediments and nutrients to coastal water bodies. Adapting our process to an open science framework will facilitate future integration of these data beyond the aquatic community.

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