510 citations · 1.3k across the 10 of their papers we have counts for
10 papers
Learning Structures in Earth Observation Data with Gaussian Processes
Fernando Mateo, Jordi Munoz-Mari, Valero Laparra +2
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian…
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…
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Katja Berger, Jochem Verrelst, Jean-Baptiste Féret +4
Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for…
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,…
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
Luca Pipia, Jordi Muñoz-Marí, Eatidal Amin +3
The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricu…