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

这个研究基础设施改善轨道-4 EPSCoR研究员（RII轨道-4）项目将提供奖学金，副教授和培训的研究生在查尔斯顿学院。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。该项目旨在通过利用最先进的卫星技术和创新算法来解决沿海地区的有害藻华问题。有害生物对海洋生态系统、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，该项目力求提供有关有害赤潮时空分布的准确信息。该项目的重要性在于它有可能彻底改变有害藻华监测和管理。传统方法在分辨率方面存在局限性，阻碍了有效的响应。推进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。有害有毒物质扰乱沿海社区，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有助于健康的沿海生态系统、安全的饮用水源和改善公众健康，从而造福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和渔业的严重影响，在全球范围内日益受到关注。由于地表水温度上升，流入河流和湖泊的营养物质增加，因此产生了有害藻华。在过去几十年中，开发了基于地球观测卫星的工具，用于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）来完成的，这是浮游植物生物量的替代指标。然而，叶绿素a本身并不能提供固有的浮游植物群落结构的完整描述或关于特定有害藻华物种的信息。因此，高分辨率的卫星海洋颜色产品，超越通用叶绿素a，并提供有关具体的有害藻华物种的信息是准确的有害藻华跟踪和预报模型的迫切需要。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强大的算法，以准确地提供有关有害赤潮分布的空间和时间明确数据。该项目将合并多类数据产品，以开发最适合与下一代高光谱传感器一起使用的HAB产品。我们将研究NASA的半解析优化方法，广义固有光学性质和新机器学习该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.