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，海冰在时空上是动态的，表现出各种演变的冰类型，需要对北极和南极的海洋活动进行分类，以便进行科学分析和业务规划。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的情况下。该项目将建立工具，使这些数据更容易获取，并降低联邦资助数据的使用门槛，特别是那些较少获得强大计算和/或教育资源的代表性不足的研究人员。为了确保广泛采用，项目组还将开发相关的互动教程和实验模块，专门为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，由于收集北极数据的遥感仪器数量的增加、模型数量的增加以及这些模型输出的变量数量的增加，现有数据的数量和种类急剧增加，为高分辨率时空海冰测绘创造了机会。这些数据的绝对数量和异质性对高效和有效的集成和分析构成了巨大的挑战。这项工作将创建合并各种不同数据产品的模块(例如，星载被动微波、来自哨兵1号、冰桥、ICESat和ICESat-2的合成孔径雷达图像，以及即将进行的NISAR任务)，并利用机器学习方法，如受限的Boltzmann机器和深度自动编码器，使这些不同类型的数据产品具有特征，从而减少数据，以便有效地代表数据，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功能构建一个模块化、半自动和交互式的可视标记平台，用于可伸缩的标记数据集创建，并将这些产品集成到EarthCube服务和用户社区的生态系统中，并将产品提供给更广泛的地球科学界。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.