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

卫星和地面遥感产生大量异构和多源时空数据。这为在全球范围内高度精确地检索地球物理参数提供了希望，但它也带来了许多与构建高效和准确检索算法相关的计算挑战。本项目正在通过开发数据挖掘方法来探索这一机会，以便：(a)改进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卫星以及AERONET地面传感器网络的气溶胶相关数据正在用于开发和验证提议的算法。所提出的工作的智力优点在于：通过使用复杂的前向模拟模型来解决这一挑战；探索大数据、异构数据和多分辨率数据的时空特性条件概率建模与不确定性估计；抽样设计;使用先进的数据结构；以及多tb数据的集成和处理。更广泛的影响源于精确气溶胶检索方法的发展，这些方法将改进气溶胶对地球能量和水循环影响的表征。此外，该项目还为在其他地球科学应用中开发准确、快速的检索算法提供指导，并将推动时空数据挖掘的发展。该项目将研究成果纳入几门课程，使不同群体的学生参与研究，并通过出版物和项目网站（www.ist.temple.edu/IIS-0612149）广泛传播研究成果，从而确保学生的广泛参与。

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).