COLLABORATIVE RESEARCH: GI CATALYTIC TRACK: Cyberinfrastructure for Intelligent High-Resolution Snow Cover Inference from Cubesat Imagery

在相关的空间和时间尺度上从太空观测地球的能力是了解水文和生态系统如何应对气候变化的关键。特别是，山区积雪覆盖地区的高时空分辨率（米尺度，日频率）观测至关重要，因为雪对水资源很重要，推动了美国西部的季节性水文制度，对生态群落产生了重大影响。Planet Labs, Inc. （Planet）是一个有前景的商业立方体卫星高分辨率图像的新来源，可用于环境科学，因为它具有高空间（3.0-4.0米）和时间（1-2天）分辨率。该项目将开发开源、基于云的网络基础设施，包括一个自动化管道，用于处理、分析和解释Planet Cubesat图像数据，使用机器学习方法来推断米尺度分辨率的积雪。所有模型和数据产品都将公开供科学界使用和修改。该项目将通过培训活动、特殊兴趣小组和孵化器项目来支持学生、博士后和其他早期职业研究人员的培训。目前，具有足够时间（日）分辨率的遥感积雪观测要么是在空间尺度上捕获的积雪太大，与高分辨率的水文和生态研究无关（例如MODIS， 500米），要么是在空间尺度上（1-10米）合适，但时间分辨率不足，成本过高（例如机载激光雷达）。近年来高时空分辨率商业对地观测数据的增加，可能会弥补地基观测数据与低分辨率卫星观测数据之间的差距。该项目将重点使用基于卷积神经网络的模型，将华盛顿、加利福尼亚和科罗拉多州三个不同山地系统的地面和空中降雪观测与行星图像相结合。这些站点覆盖了NASA机载雪观测站（ASO）和SnowEx任务收集的高分辨率（3m）地面和空中雪观测数据，这些数据将用于模型的训练和验证。该项目将开发先进的网络基础设施，使用可扩展的虚拟机、分布式协作架构、可重用的计算框架和可复制的机器学习工作流程，使地球科学家能够访问、处理并从Cubesat数据中生成高分辨率的雪产品。该项目将采用开源策略，确保所有数据、算法和架构符合FAIR数据原则和可重复性，并将包括促进基础设施和工具采用的培训材料。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。

The ability to observe the Earth from space at relevant spatial and temporal scales is key to understanding how hydrological and ecological systems will respond to climate change. In particular, high spatial and temporal resolution (meter scale, daily frequency) observations of snow-covered areas in mountain regions are critical as snow is important for water resources, driving the seasonal hydrological regimes of the Western U.S., with significant impacts on ecological communities. Planet Labs, Inc. (Planet) is a promising new source of commercial Cubesat high-resolution imagery that can be used in environmental science, as it has both high spatial (3.0-4.0 m) and temporal (1-2 day) resolution. This project will develop open-source, cloud-based cyberinfrastructure including an automated pipeline for processing, analyzing and interpreting Planet Cubesat image data using a machine learning approach to infer snow cover at meter-scale resolution. All models and data products will be openly available for use and modification by scientific communities. The project will support the training of students, postdocs and other early-career researchers through training events, special interest groups, and incubator programs. Currently, remotely-sensed snow observations with adequate temporal (daily) resolution are either captured at a spatial scale far too large to be relevant to high-resolution hydrology and ecology studies (e.g. MODIS, 500m) or are appropriate in spatial scale (1-10 m) but have inadequate temporal resolution and are cost-prohibitive (e.g. airborne LiDAR). The recent increase of commercial Earth Observation data with high spatiotemporal resolution may bridge the gap between ground-based and low-resolution satellite observation data. This project will focus on using convolutional neural networks-based models to couple ground and airborne-derived snow observations with Planet imagery in three different montane systems in Washington, California, and Colorado. These sites have very good coverage of ground and airborne snow observations at high resolution (3m) collected by the NASA Airborne Snow Observatory (ASO) and SnowEx missions, which will be used in the training and validation of the models. The project will develop advanced cyberinfrastructure using scalable virtual machines, distributed collaborative architecture, reusable computational frameworks, and replicable machine learning workflows to empower Earth scientists to access, process and generate high-resolution snow products from Cubesat data. The project will adopt open-source strategies and ensure that all data, algorithms, and architecture comply with FAIR data principles and reproducibility and will include training materials that promote the adoption of the infrastructure and tools.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.