Collaborative Research: EarthCube Data Capabilities: Enabling analysis of heterogeneous, multi-source cryospheric data

海冰是气候系统的重要组成部分，也是气候变化的指示器。海冰数据产品用于各种地球科学，包括物理和生物海洋学、气候学和气象学。由于海流、风、温度波动以及地方和全球气候模式的综合影响，海冰在时空上是动态的，呈现出各种不断变化的冰型，需要进行分类，以便进行科学分析，并为北极和南极的海洋活动进行业务规划。然而，海冰的测绘和分类仍然是一项科学挑战，特别是在高空间和时间分辨率方面。 该项目将建立工具，使这些数据更容易获得，并降低使用联邦资助数据的障碍，特别是代表性不足的研究人员，他们无法获得强大的计算和/或教育资源。为了确保广泛采用，项目团队还将开发相关的交互式教程和实验室模块，专为那些在适用于地球科学的数据科学方法方面几乎没有背景的学生设计。近年来，由于在北极上空收集数据的遥感仪器数量增加、模型数量和这些模型输出的变量数量增加，现有数据的数量和种类都有了急剧增加，为高分辨率时空海冰测绘创造了机会。这些数据的庞大数量和异质性对高效和有效的集成和分析构成了重大挑战。这项工作将创建用于组合异构数据产品的模块（例如，卫星无源微波、Sentinel-1、IceBridge、ICESat和ICESat-2的SAR图像以及即将到来的NISAR使命），并使用机器学习方法（如受限玻尔兹曼机和深度自动编码器）对这些异构数据产品进行特征化，以减少数据，从而有效地表示数据，同时减少数据的大小和维度。该项目将构建一个模块化、半自动化和交互式的可视化标签平台，利用深度学习模型生成的功能创建可扩展的标签数据集，并将这些产品整合到EarthCube服务和用户社区的生态系统中。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

Sea ice is an important component of the climate system and an indicator of climate change. Sea ice data products are used in a variety of geosciences including physical and biological oceanography, climatology and meteorology. As a result of the combined effect of currents, winds, temperature fluctuations, and local and global climate patterns, sea ice is spatiotemporally dynamic, exhibiting a variety of evolving ice types that need classification for scientific analysis as well as operational planning for marine activities in the Arctic and Antarctic. The mapping and classification of sea ice, however, remains a scientific challenge, especially at high spatial and temporal resolutions. This project will build tools to make these data more readily accessible and lower barriers to the usage of federally funded data, especially by underrepresented researchers with less access to strong computational and/or educational resources. To ensure wide adoption, the project team will also develop related interactive tutorials and lab modules designed for students with little to no background in data science methods applicable to geoscience. In recent years, there has been a dramatic increase in the volume and variety of available data, due to both increases in the number of remote sensing instruments collecting data over the Arctic, growth in the number of models and the number of variables output by these models, creating an opportunity for high-resolution spatiotemporal sea ice mapping. The sheer volume and heterogeneity of such data pose a significant challenge to efficient and effective integration and analysis. The work will create modules for combining heterogeneous data products (e.g., satellite-borne passive microwave, SAR imagery from Sentinel-1, IceBridge, ICESat and ICESat-2 and the upcoming NISAR mission) and enable featurization of these heterogeneous data products using machine learning methods such as Restricted Boltzmann Machines and Deep Autoencoders to reduce the data to effectively represent the data while reducing size and dimensionality of the data. The project will build a modularized, semi-automatic, and interactive visual labeling platform utilizing the features generated by deep learning models for scalable labeled dataset creation, and integrate these products into the ecosystem of EarthCube services and user communities, and make the products available to the broader geoscience community.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.