Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploit...

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Main Authors:	Migliavacca, Mirco, Meroni, Michele, Busetto, Lorenzo, Colombo, Roberto, Zenone, Terenzio, Matteucci, Giorgio, Manca, Giovanni, Seufert, Guenther
Format:	Online
Language:	English
Published:	Molecular Diversity Preservation International (MDPI) 2009
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280840/

id	pubmed-3280840
recordtype	oai_dc
spelling	pubmed-32808402012-03-07 Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model Migliavacca, Mirco Meroni, Michele Busetto, Lorenzo Colombo, Roberto Zenone, Terenzio Matteucci, Giorgio Manca, Giovanni Seufert, Guenther Article In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale. Molecular Diversity Preservation International (MDPI) 2009-02-13 /pmc/articles/PMC3280840/ /pubmed/22399948 http://dx.doi.org/10.3390/s90200922 Text en © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Migliavacca, Mirco Meroni, Michele Busetto, Lorenzo Colombo, Roberto Zenone, Terenzio Matteucci, Giorgio Manca, Giovanni Seufert, Guenther
spellingShingle	Migliavacca, Mirco Meroni, Michele Busetto, Lorenzo Colombo, Roberto Zenone, Terenzio Matteucci, Giorgio Manca, Giovanni Seufert, Guenther Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
author_facet	Migliavacca, Mirco Meroni, Michele Busetto, Lorenzo Colombo, Roberto Zenone, Terenzio Matteucci, Giorgio Manca, Giovanni Seufert, Guenther
author_sort	Migliavacca, Mirco
title	Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
title_short	Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
title_full	Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
title_fullStr	Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
title_full_unstemmed	Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
title_sort	modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model
description	In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.
publisher	Molecular Diversity Preservation International (MDPI)
publishDate	2009
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280840/
_version_	1611506478056734720

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

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