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.
MAPPING LAI AND CHLOROPHYLL CONTENT FROM AT-SENSOR APEX DATA USING A BAYESIAN OPTIMISATION OF A COUPLED CANOPY-ATMOSPHERE MODEL
ABSTRACTThis 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 atsensor 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.