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

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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.