Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyl...

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Bibliographic Details
Main Authors: Pablo Reyes-Muñoz, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Remote Sensing
Subjects:
vegetation traits
Sentinel-3
TOA radiance
OLCI
Gaussian process regression
machine learning
Online Access:https://www.mdpi.com/2072-4292/14/6/1347

Internet

https://www.mdpi.com/2072-4292/14/6/1347

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