Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image

80 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) (2015, background): “…seamless mosaicking of several APEX ﬂ ight lines, while minimizing angular effects dominating ﬂ ight patterns that are chosen to comply with minimal operational constraints and not optimized for minimal directional illumination in ﬂ u-ences (Laurent, Schaepman, Verhoef, Weyermann, & Chavez, 2014).”
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, methodology): “…Vincini et al., 2014) to machine learning regression algorithms (Verrelst et al., 2012b, 2013b; Rivera Caicedo et al., 2014a) until the inversion of radiative transfer methods (Richter et al., 2009; Atzberger and Richter, 2012; Rivera et al., 2013; Laurent et al., 2014; Verrelst et al., 2014).”
Sentinel-2 Data for Land Cover/Use Mapping: A Review (2020, background): “[106] Canopy, brown leaves, green leaves OBIA-Classifier Bayesian 96–98 Lu, et al.”
Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, background): “If the animal is marked individually (e.g. with number tags, combinations of coloured marks/rings) or can be 324 A. Lausch et al. / Ecological Indicators 70 (2016) 317–339 A. Lausch et al. / Ecological Indicators 70 (2016) 317–339 325 F high-r P d i o t b w i t s s s s i w r t a o b e 2 d o w e v d ”