A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data

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Green area index from an unmanned aerial system over wheat and rapeseed crops (2014, methodology): “GAI may be also estimated using a radiative transfer model inversion scheme (Laurent et al., 2013; Verger, Baret, & Camacho de Coca, 2011; Weiss et al., 2000).”
Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image (2014, methodology): “In order to obtain the most accurate LAI and Cab estimates as possible from this single image, the Bayesian object-based approach of Laurent et al. (2013) was chosen, because it combines the strengths of model coupling, a priori data and spatial constraints regularization methods.”
Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) (2015, methodology): “tion (Laurent et al., 2013); and D) forward simulated at-sensor-radiances using 3D modeling ( and the University of Zurich providing instrument operators for data flights.”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “Examples of these multiple output inversion mapping routines have been described by Lauvernet et al. (2008) and Laurent et al. (2013).”