510 citations · 971 across the 7 of their papers we have counts for
7 papers
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
Jorge Vicent, Jochem Verrelst, Juan Pablo Rivera-Caicedo +4
Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere. Because these mo…
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
Jochem Verrelst, Juan Pablo Rivera, Anatoly Gitelson +3
With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized…
Statistical Learning for End-to-End Simulations
J. Vicent, J. Verrelst, J. P. Rivera-Caicedo +4
End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the gen…
Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data
Jose Estevez, Jorge Vicent, Juan Pablo Rivera-Caicedo +6
Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithm…
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Jorge Vicent, Luis Alonso, Luca Martino +3
Atmospheric correction of Earth Observation data is one of the most critical steps in the data processing chain of a satellite mission for successful remote sensing applications. A…
Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods
Jochem Verrelst, Zbyněk Malenovský, Christiaan Van der Tol +5
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream…
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
ochem Verrelst, Sara Dethier, Juan Pablo Rivera +3
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However,…