Using a Deployable Analysis Environment to Study Continental-Scale Phenology at High-Spatial Resolution

The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and...

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Bibliographic Details
Main Authors: Nattino, Francesco, Izquierdo-Verdiguier, Emma, Chandramouli, Pranav, Grootes, Meiert W., Ku, Ou, Alidoost, Fakhereh, Zurita-Milla, Raul
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Computational modeling
Data models
Distributed databases
Geoscience and remote sensing
Phenology
Spatial resolution
Online Access:Request full text
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Summary:The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10640587