Integration of flux tower data and remotely sensed data into the SCOPE simulator: A Bayesian approach

0522666 - ÚVGZ 2020 CZ eng A - Abstract
Raj, Rahul - Lukeš, Petr - Homolová, Lucie - Brovkina, Olga - Šigut, Ladislav - Bagher, B.
Integration of flux tower data and remotely sensed data into the SCOPE simulator: A Bayesian approach.
The 3rd ICOS Science Conference. Praha, 2018. s. 131-132.
[The 3rd ICOS Science Conference. 11.09.2018-13.09.2018, Praha]
Institutional support: RVO:86652079
Keywords : scope * forest * gross primary productivity * modelling
OECD category: Remote sensing

Quantification of gross primary production (GPP) together with the continuous monitoring of its temporal variations are indispensable to obtain reliable data for indicating the capacity of forests to sequester carbon. GPP can be quantified us ing two sources: (a) process-based simulator (PBS); and (b) flux tower measurements of the net ecosystem exchange (NEE) of CO2. Additionally, remotely sensed optical data, which can be linked to the vegetation properties, carry valuable information to express canopy photosynthesis (i.e., GPP). A PBS has an advantage over flux tower and remotely sensed optical data because it can be run at time scales beyond the limit of direct measurements. Simulation of GPP by PBS at a high accuracy, however, depends up on how well the parameterization is achieved. A process-based simulator SCOPE (Soil-Canopy-Observation of Photosynthesis and Energy balance) links top of canopy observations of radiance with land surface processes (that include GPP simulation). Some parameters of SCOPE are difficult to obtain from field observations. Reliable estimates of parameters can, however, be obtained using calibration against observations of output. In this study, we present a Bayesian framework to calibrate SCOPE simulator against the estimates of GPP (separated from NEE), and the top of canopy
Permanent Link: http://hdl.handle.net/11104/0307937