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

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

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and lev...

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Bibliographic Details
Main Authors: Ana B. Pascual-Venteo, Jose L. Garcia, Katja Berger, José Estévez, Jorge Vicent, Adrián Pérez-Suay, Shari Van Wittenberghe, Jochem Verrelst
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Remote Sensing
Subjects:
top-of-atmosphere
atmospheric correction
machine learning models
Gaussian processes
radiative transfer model
SCOPE
Online Access:https://www.mdpi.com/2072-4292/16/7/1211

Internet

https://www.mdpi.com/2072-4292/16/7/1211

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