Paper Info
Title
Mapping LAI and chlorophyll content from at-sensor APEX data using a Bayesian optimisation of a coupled canopy-atmosphere model
Abstract
This contribution proposes a methodological approach based on a coupled canopy-atmosphere radiative transfer model and a Bayesian optimization algorithm, which allows the use of a priori data in the retrieval. This approach was used to estimate LAI and leaf chlorophyll content (Cab) in the agricultural test site Oensingen, Switzerland, from at-sensor radiance data of the new airborne APEX imaging spectrometer. The Bayesian optimization allowed having up to 7 free variables in the optimization. The obtained maps of estimated LAI and Cab values at the field level show a good agreement with our expectations in terms of the values themselves, but also their variation range and spread.
Year
DOI
Venue
2012
10.1109/IGARSS.2012.6352321
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
geochemistry,radiative transfer,remote sensing,vegetation,vegetation mapping,Airborne Prism EXperiment,Bayesian optimization algorithm,LAI mapping,Oensingen,Switzerland,agricultural test site,airborne APEX imaging spectrometer,at-sensor APEX data,chlorophyll content mapping,coupled canopy-atmosphere radiative transfer model,leaf chlorophyll content,APEX,Bayesian optimization,at-sensor radiance,canopy-atmosphere coupling,radiative transfer
Meteorology,Imaging spectrometer,Computer science,Remote sensing,Bayesian optimization,A priori and a posteriori,Atmospheric radiative transfer codes,Atmospheric model,Radiative transfer,Radiance,Bayesian probability
Conference
ISSN
ISBN
Citations 
2153-6996 E-ISBN : 978-1-4673-1158-8
978-1-4673-1158-8
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Valerie C. E. Laurent100.34
w verhoef26914.73
Michael E. Schaepman315840.02
Alexander Damm4217.75
Jan G. P. W. Clevers515319.42