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，是气候变化的指标。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、温度波动以及当地和全球气候模式的综合影响，海冰是时空动态的，表现出各种不断演变的冰类型，需要对其进行分类，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，海冰的制图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下。该项目将建立工具，使这些数据更容易获取，并降低使用联邦资助数据的障碍，特别是对于那些缺乏强大计算和/或教育资源的代表性不足的研究人员。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模块，专为那些几乎没有适用于地球科学的数据科学方法背景的学生设计。近年来，由于收集北极上空数据的遥感仪器数量的增加、模式数量的增加以及这些模式输出的变量数量的增加，可用数据的数量和种类急剧增加，为高分辨率时空海冰制图创造了机会。这些数据的庞大数量和异质性对高效和有效的整合和分析构成了重大挑战。该工作将创建用于组合异构数据产品的模块（例如，星载无源微波，来自Sentinel-1， IceBridge， ICESat和ICESat-2的SAR图像以及即将到来的NISAR任务），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）实现这些异构数据产品的特征化，以减少数据以有效地表示数据，同时减少数据的大小和维度。该项目将利用深度学习模型生成的特征，构建一个模块化、半自动和交互式的可视化标记平台，用于创建可扩展的标记数据集，并将这些产品集成到EarthCube服务和用户社区的生态系统中，并使这些产品可用于更广泛的地球科学社区。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。

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.