Catherine Prigent
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- National Center for Scientific Research, Paris Cité University, PSL Research University, Paris Observatory, PSL University, Sorbonne Paris Cité University (group), Laboratoire d'études du rayonnement et de la matière en astrophysique et atmosphères, Laboratory of Space Studies and Instrumentation in Astrophysics, Shell (France), LIRA - Laboratoire d'instrumentation et de recherche en astrophysiqueas of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
🔖 Identifiers · 2
- ORCID iD
- 0000-0003-0266-1403
- OpenAlex ID
- A5037279767, A5125162139, A5134467097as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Affiliated with 7
Author of 6
A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations,
Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution,
Dimension reduction of satellite observations for remote sensing. Part 1: A comparison of compression, channel selection and bottleneck channel approaches,
Soil Moisture Retrieval Using Neural Networks: Application to SMOS,
Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography),
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence
← Authored by 6
A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations,
Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution,
Dimension reduction of satellite observations for remote sensing. Part 1: A comparison of compression, channel selection and bottleneck channel approaches,
Soil Moisture Retrieval Using Neural Networks: Application to SMOS,
Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography),
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence
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Summary
Catherine Prigent is a human[1].