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  Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Pascual-Venteo, A. B., Garcia, J. L., Berger, K., Estévez, J., Vicent, J., Pérez-Suay, A., Van Wittenberghe, S., Verrelst, J. (2024): Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery. - Remote Sensing, 16, 7, 1211.
https://doi.org/10.3390/rs16071211

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Pascual-Venteo, Ana B.1, Author
Garcia, Jose L.1, Author
Berger, Katja2, Author              
Estévez, José1, Author
Vicent, Jorge1, Author
Pérez-Suay, Adrián1, Author
Van Wittenberghe, Shari1, Author
Verrelst, Jochem1, Author
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1External Organizations, ou_persistent22              
21.2 Global Geomonitoring and Gravity Field, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146026              

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 Dates: 2024-03-292024
 Publication Status: Finally published
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 Identifiers: DOI: 10.3390/rs16071211
GFZPOF: p4 T5 Future Landscapes
OATYPE: Gold Open Access
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Title: Remote Sensing
Source Genre: Journal, SCI, Scopus, OA
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Pages: - Volume / Issue: 16 (7) Sequence Number: 1211 Start / End Page: - Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals426
Publisher: MDPI