Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study

By Valerie C. E. Laurent, W. Verhoef, Jan G. P. W. Clevers, Michael E. Schaepman. 2011

65 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/d701295d43078cc56ffa0760717ccf7083534a8e

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Citations in the corpus

Review of optical-based remote sensing for plant trait mapping (2013, background): “Further, when combined with atmospheric 254 RTMs, they hold the promise to estimate many leaf traits and canopy properties 255 simultaneously (Laurent et al., 2011a).”
Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, methodology): “Advanced retrieval methods, such as coupled models (Laurent et al., 2011b), multiangular observations (Laurent et al., 2011a), object-based (Laurent et al., 2013) and Bayesian inversion schemes 3 l Indic ( r 4 o G t t v D p o s e t l s w a a a t a 4 u o T u L i e L t 2 s V e d s s a s p f p L a c a…”
Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image (2014, methodology): “J contains the partial derivatives of the model, obtained by varying each variable, by 1% of its allowed range (Laurent et al., 2011a).”
Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) (2015, methodology): “…based algorithms for the APEX product retrieval (e.g., Bayesian optimization algorithm in combination with coupled atmosphere – canopy models; Laurent et al., 2011b), and the comprehensive data quality assessment of APEX (i.e., calibration in CHB, IFC monitoring) enables to trace…”
A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data (2013, methodology): “In each column, J contains the partial derivatives of the model, obtained by varying each variable by 1% of its allowed range (Laurent et al., 2011a).”