Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data

Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data

In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, ho...

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Bibliographic Details
Main Authors: Ana B. Pascual-Venteo, Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Remote Sensing
Subjects:
PRISMA
CHIME
hybrid methods
biochemical and biophysical traits
Gaussian process regression
active learning
Online Access:https://www.mdpi.com/2072-4292/14/10/2448

Internet

https://www.mdpi.com/2072-4292/14/10/2448

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