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

这项研究基础设施改善轨道4 EPSCoR研究人员（RII轨道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。这项工作将与美国宇航局艾姆斯研究中心（ARC）的研究人员合作进行。该项目旨在利用最先进的卫星技术和创新算法，解决沿海地区的有害藻华问题。有害藻华对海洋生态系统、人类健康和地方经济构成重大威胁。通过利用新一代卫星传感器和先进的数据分析，该项目旨在提供有关赤潮的时空分布的准确信息。这个项目的意义在于它有可能彻底改变有害藻华的监测和管理。传统的方法在分辨率上有局限性，阻碍了有效的反应。推进卫星遥感和数据分析，促进科学进步。此外，该项目通过促进环境健康、繁荣和福利，符合国家利益。有害藻华破坏沿海社区，由于有害毒素影响渔业、旅游业和人类健康。及时监测可以减轻这些影响。该项目通过促进来自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，推进科学知识。最终，该项目通过实施有效的环境政策和管理使社会受益。了解有害藻华的动态将导致更健康的沿海生态系统，更安全的饮用水源，改善公众健康，有利于国家的福祉和繁荣。有害藻华（HABs）是水生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业和渔业的严重影响而日益引起全世界的关注。有害藻华的发生是对地表水温度上升和营养物流入溪流和湖泊增加的反应。在过去的几十年里，基于地球观测卫星的工具已经被开发出来，用于跟踪赤潮和藻华的发展和运动。这在传统上是通过估算叶绿素-a浓度（Chl-a, mg - m - 3）来完成的，叶绿素-a是浮游植物生物量的代表。然而，单独的Chl-a并不能提供对固有浮游植物群落结构的完整描述或有关特定HAB物种的信息。因此，高分辨率的卫星海洋颜色产品，超越通用的Chl-a，提供特定有害藻华物种的信息，对于准确的有害藻华跟踪和预测模型至关重要。该项目的目标是利用新一代高光谱传感器，并支持开发能够准确提供关于赤潮分布的空间和时间明确数据的强大算法的持续努力。该项目将合并多个类别的数据产品，以开发最适合下一代高光谱传感器使用的HAB产品。我们将研究NASA的半分析优化方法、广义固有光学特性和新的基于机器学习的HAB映射算法，这些算法可用于表征水的光学特性，并从几个现有和未来的海洋彩色卫星中检索HAB产品。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.