Record link: 
http://hdl.handle.net/20.500.12708/218340
-
Title: 
Neural Spatiotemporal Interpolation: A Scalable Deep Learning Framework for Filling Gaps in GNSS-R Soil Moisture Data
en
Citation: 
Costa Pinho Alves, O., Wagner, W., Santi, E., Unwin, M., & Marigold, G. (2025, June 27). Neural Spatiotemporal Interpolation: A Scalable Deep Learning Framework for Filling Gaps in GNSS-R Soil Moisture Data [Conference Presentation]. Living Planet Symposium (LPS 2025), Wien, Austria. http://hdl.handle.net/20.500.12708/218340
-
Publication Type: 
Presentation - Conference Presentation
en
Language: 
English
-
Authors: 
Costa Pinho Alves, Oto 
Wagner, Wolfgang 
Santi, Emanuele 
Unwin, Martin 
Marigold, Gabrielle 
-
Organisational Unit: 
E120-01 - Forschungsbereich Fernerkundung 
-
Date (published): 
27-Jun-2025
-
Event name: 
Living Planet Symposium (LPS 2025) 
en
Event date: 
23-Jun-2025 - 27-Jun-2025
-
Event place: 
Wien, Austria
-
Keywords: 
GNSS-R; soil moisture; remote sensing
en
Research Areas: 
Environmental Monitoring and Climate Adaptation: 100%
-
Science Branch: 
2074 - Geodäsie, Vermessungswesen: 70%
1020 - Informatik: 15%
1054 - Physische Geographie: 15%
-
Appears in Collections:
Presentation

Show full item record

Google ScholarTM

Check