Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning by Veronika Döpper, Alby Duarte Rocha, Katja Berger, Tobias Gränzig, Jochem Verrelst, Birgit Kleinschmit, Michael Förster

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Retrieval of aboveground crop nitrogen content with a hybrid machine learning method by Katja Berger, Jochem Verrelst, Jean-Baptiste Féret, Tobias Hank, Matthias Wocher, Wolfram Mauser, Gustau Camps-Valls

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Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery by Ana B. Pascual-Venteo, Jose L. Garcia, Katja Berger, José Estévez, Jorge Vicent, Adrián Pérez-Suay, Shari Van Wittenberghe, Jochem Verrelst

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Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model by Katja Berger, Clement Atzberger, Martin Danner, Matthias Wocher, Wolfram Mauser, Tobias Hank

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Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy by Martin Danner, Katja Berger, Matthias Wocher, Wolfram Mauser, Tobias Hank

“…Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun–target–sensor-geometries. …”
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A comprehensive survey on quantifying non-photosynthetic vegetation cover and biomass from imaging spectroscopy by Jochem Verrelst, Andrej Halabuk, Clement Atzberger, Tobias Hank, Stefanie Steinhauser, Katja Berger

“…Given three decades of spectroscopy studies on NPV or CR cover detection, we identify the following methodological trends: (1) a shift from unmixing approaches towards regression-based models; (2) a shift from two-band indices towards multi-band equations; and (3) a shift from linear regression towards data-driven machine learning models. (4) In addition, gradual progress in radiative transfer modelling (RTM) in describing the interaction of radiation with non-photosynthetic plant material has been achieved. …”
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A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data by Katja Berger, Juan Pablo Rivera Caicedo, Luca Martino, Matthias Wocher, Tobias Hank, Jochem Verrelst

“…Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. …”
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Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine by Pablo Reyes-Muñoz, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo, Jochem Verrelst

“…The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. …”
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Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data by Ana B. Pascual-Venteo, Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo, Jochem Verrelst

“…Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. …”
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