RII Track-4: @NASA: Next Generation Hyperspectral Approaches to Detect Harmful Algal Blooms

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project will provide a fellowship to an Associate Professor and training for a graduate student at the College of Charleston. This work would be conducted in collaboration with researchers at the NASA Ames Research Center (ARC). The project aims to address Harmful Algal Blooms (HABs) in coastal regions by utilizing state-of-the-art satellite technology and innovative algorithms.

HABs pose significant threats to marine ecosystems, human health, and local economies. By leveraging new-generation satellite sensors and advanced data analysis, the project seeks to provide accurate information on the spatial and temporal distribution of HABs. This project's significance lies in its potential to revolutionize HAB monitoring and management.

Traditional methods have limitations in resolution, hindering effective responses. Advancing satellite remote sensing and data analysis contributes to scientific progress. Furthermore, the project serves the national interest by promoting environmental health, prosperity, and welfare.

HABs disrupt coastal communities, affecting fisheries, tourism, and human health due to harmful toxins. Timely monitoring can mitigate these impacts. The project supports education and diversity by fostering collaboration among researchers from diverse backgrounds.

Interdisciplinary opportunities attract scholars, advancing scientific knowledge. Ultimately, the project benefits society by enabling effective environmental policies and management. Understanding HAB dynamics will lead to healthier coastal ecosystems, safer drinking water sources, and improved public health, benefiting the nation's well-being and prosperity.

Harmful algal blooms (HABs) are causing one of the most important environmental crises in aquatic environments and are of growing concern worldwide due to their serious impacts on public health as well as on the aquaculture and fisheries industry. HABs occur in response to rising surface water temperatures and increased flux of nutrients into streams and lakes.

Over the past few decades, earth-observation satellite-based tools have been developed for tracking the development and movement of HABs and algal blooms. This has traditionally been done using estimates of chlorophyll-a concentration (Chl-a, mg m−3), a proxy for phytoplankton biomass. However, Chl-a alone does not provide a full description of the inherent phytoplankton community structure or information regarding specific HAB species.

Thus, high-resolution satellite ocean color products that go beyond universal Chl-a and provide information on specific HAB species are critically needed for accurate HAB tracking and forecasting models. The goal of the project is to utilize new-generation hyperspectral sensors and support the continued effort of developing robust algorithms that can accurately provide spatially and temporally explicit data on the distribution of HABs.

The project will merge multiple classes of data products to develop HAB products that are best suited for use with next-generation hyperspectral sensors. We will work on NASA’s semi-analytical optimization method, the Generalized Inherent Optical Property and new machine learning-based HAB mapping algorithms which can be used to characterize the optical properties of water and retrieve HAB products from several existing and future ocean color satellites.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.