Collaborative Research: Data Mining Support for Retrieval and Analysis of Geophysical Parameters

协作研究：数据挖掘支持地球物理参数检索和分析

• 批准号: 0611892
• 负责人: Zhanqing Li
• 金额: $ 20.02万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0611892&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Mining; Support; Collaborative; Research; Data; Mining; Support

Satellite and ground-based remote sensing produces large quantities of heterogeneous and multisource spatial-temporal data. This offers promise for highly accurate retrievals of geophysical parameters on a global scale, but it also opens a number of computational challenges related to the construction of efficient and accurate retrieval algorithms. This project is exploring this opportunity through development of data mining methods to: (a) improve existing single-sensor retrieval algorithms; and (b) allow high-quality joint-sensor retrieval. Aerosol-related data from the TERRA and AQUA satellites and the AERONET network of ground-based sensors is being used for development and validation of the proposed algorithms. The intellectual merit of the proposed work is in: addressing this challenge through the use of complex forward-simulation models; exploring spatial-temporal properties of large, heterogeneous and multi-resolution data; conditional probability modeling and uncertainty estimation; sampling design; use of advanced data structures; and integration and handling of multi-TB data. The broader impacts stem from development of accurate aerosol retrieval methods that will allow improved characterization of the effects of aerosols on the Earth's energy and water cycles. Additionally, the project is providing guidelines for developing accurate and fast retrieval algorithms in other geoscience applications, and will lead to advancements in spatial-temporal data mining. The project is assuring a broad participation of students through incorporating the research results into several courses, exposing diverse groups of students to research, and widely disseminating the results through publications and the project web site (www.ist.temple.edu/IIS-0612149).

卫星和基于地面的遥感会产生大量的异质和多源空间数据。这为在全球范围内高度准确地检索地球物理参数提供了希望，但它也打开了许多计算挑战，这些挑战与有效，准确的检索算法的构建有关。该项目通过开发数据挖掘方法来探索这一机会：（a）改善现有的单传感器检索算法； （b）允许高质量的关节传感器检索。来自Terra和Aqua卫星和地面传感器的Aeronet网络的与气雾相关的数据正在用于开发和验证所提出的算法。拟议工作的智力优点在于：通过使用复杂的前瞻性模型来应对这一挑战；探索大型，异质和多分辨率数据的时空特性；条件概率建模和不确定性估计；采样设计；使用高级数据结构；以及集成和处理多TB数据。更广泛的影响源于准确的气溶胶检索方法的发展，这些方法将改善气溶胶对地球能量和水周期的影响的表征。此外，该项目还提供了在其他地球科学应用中开发准确，快速检索算法的准则，并将导致空间数据挖掘的进步。该项目通过将研究结果纳入多个课程，使学生进行研究，并通过出版物和项目网站（www.ist.temple.edu/iis-0612149）广泛传播结果，从而确保学生的广泛参与。