The SCOPE model embeds the state of art for coupling soil vegetation atmosphere transfer (SVAT) and radiative transfer models (RTM). For that reason the FLuorescence EXplorer (FLEX) mission selected this model to derive vegetation properties through inversion. However inverse problem is often ill-posed, providing equally likely solutions and hence inflating the uncertainty of the retrieved parameters. In this work we test the use of different priors based on ancillary measurements and literature to support multiple-constrain inversion of SCOPE. Results show that prior information on the relationships between variables such as leaf chlorophyll content (Cab), leaf carotenoids content (Cca), leaf water content (Cw) and/or maximum carboxylation rate (Vcmax) reduce inversion uncertainties and overfitting, and should be sampled/estimated together with optical data.
Pacheco-Labrador, J., Carvalhais, N., Perez-Priego, O., El-Madany, T., Rossini, M., Julitta, T., et al. (2018). Assessing the use of multiple constraints and ancillary data to support scope model inversion in a experimental grassland. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.5975-5978). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2018.8518487].
Assessing the use of multiple constraints and ancillary data to support scope model inversion in a experimental grassland
Rossini M.;Julitta T.;Migliavacca M.
2018
Abstract
The SCOPE model embeds the state of art for coupling soil vegetation atmosphere transfer (SVAT) and radiative transfer models (RTM). For that reason the FLuorescence EXplorer (FLEX) mission selected this model to derive vegetation properties through inversion. However inverse problem is often ill-posed, providing equally likely solutions and hence inflating the uncertainty of the retrieved parameters. In this work we test the use of different priors based on ancillary measurements and literature to support multiple-constrain inversion of SCOPE. Results show that prior information on the relationships between variables such as leaf chlorophyll content (Cab), leaf carotenoids content (Cca), leaf water content (Cw) and/or maximum carboxylation rate (Vcmax) reduce inversion uncertainties and overfitting, and should be sampled/estimated together with optical data.
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