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

研究基础设施改进Track-4 EPSCoR研究人员(RII Track-4)项目将为查尔斯顿学院的一名副教授提供奖学金，并为一名研究生提供培训。这项工作将与NASA艾姆斯研究中心(ARC)的研究人员合作进行。该项目旨在通过利用最先进的卫星技术和创新算法来应对沿海地区有害的藻类水华(HAB)。赤潮对海洋生态系统、人类健康和当地经济构成重大威胁。通过利用新一代卫星传感器和先进的数据分析，该项目寻求提供关于赤潮时空分布的准确信息。这个项目的重要意义在于它有可能彻底改变卫生署的监测和管理。传统的方法在分辨率上存在局限性，阻碍了有效的应对。先进的卫星遥感和数据分析有助于科学进步。此外，该项目通过促进环境健康、繁荣和福利来服务于国家利益。由于有害毒素，赤潮破坏了沿海社区，影响了渔业、旅游业和人类健康。及时监测可以减轻这些影响。该项目通过促进不同背景的研究人员之间的合作来支持教育和多样性。跨学科的机会吸引学者，推动科学知识的发展。最终，该项目通过实施有效的环境政策和管理使社会受益。了解赤潮的动态变化将导致更健康的沿海生态系统、更安全的饮用水水源和改善公共健康，有利于国家的福祉和繁荣。赤潮正在造成水环境中最重要的环境危机之一，由于其对公众健康以及水产养殖和渔业的严重影响，在世界范围内引起越来越多的关注。赤潮的发生是对地表水温上升和流入溪流和湖泊的营养物质流量增加的反应。在过去的几十年里，已经开发了基于地球观测卫星的工具来跟踪赤潮和藻华的发展和移动。传统上，这是通过估算叶绿素a浓度(Chl-a，mg m−3)来实现的，这是浮游植物生物量的一个替代指标。然而，Chl-a本身并不能完全描述固有的浮游植物群落结构，也不能提供有关特定赤潮生物物种的信息。因此，对于准确的赤潮跟踪和预报模型来说，迫切需要超越通用Chl-a并提供特定赤潮物种信息的高分辨率卫星海洋颜色产品。该项目的目标是利用新一代高光谱传感器，并支持继续努力开发稳健的算法，以准确地提供关于赤潮分布的空间和时间上的明确数据。该项目将合并多个类别的数据产品，以开发最适合与下一代高光谱传感器一起使用的HAB产品。我们将致力于NASA的半解析优化方法、广义固有光学属性和新的基于机器学习的HAB映射算法，这些算法可以用来表征水的光学属性，并从几个现有和未来的海洋颜色卫星中检索HAB产品。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.