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

Pascual-Venteo, Ana B.; Garcia, Jose L.; Berger, Katja; Estévez, José; Vicent, Jorge; Pérez-Suay, Adrián; Van Wittenberghe, Shari; Verrelst, Jochem

doi:10.3390/rs16071211

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

Authors

Pascual-Venteo, Ana B.
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Garcia, Jose L.
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/persons/resource/kberger

Berger, Katja
1.2 Global Geomonitoring and Gravity Field, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Estévez, José
External Organizations;

Vicent, Jorge
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Pérez-Suay, Adrián
External Organizations;

Van Wittenberghe, Shari
External Organizations;

Verrelst, Jochem
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Citation

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

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026313

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