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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| Format: | Article |
| Language: | English |
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MDPI AG
2009-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/9/2/922/ |
| _version_ | 1798006052491362304 |
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| author | Guenther Seufert Giovanni Manca Giorgio Matteucci Terenzio Zenone Lorenzo Busetto Roberto Colombo Michele Meroni Mirco Migliavacca |
| author_facet | Guenther Seufert Giovanni Manca Giorgio Matteucci Terenzio Zenone Lorenzo Busetto Roberto Colombo Michele Meroni Mirco Migliavacca |
| author_sort | Guenther Seufert |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T12:49:18Z |
| format | Article |
| id | doaj.art-aebc386041304fc18e6e92d9153d4aa2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:49:18Z |
| publishDate | 2009-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aebc386041304fc18e6e92d9153d4aa22022-12-22T04:23:15ZengMDPI AGSensors1424-82202009-02-019292294210.3390/s90200922Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based ModelGuenther SeufertGiovanni MancaGiorgio MatteucciTerenzio ZenoneLorenzo BusettoRoberto ColomboMichele MeroniMirco MigliavaccaIn 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.http://www.mdpi.com/1424-8220/9/2/922/Gross Primary ProductionPhenologyBIOME-BGCPROSPECTSAILHPoplar plantations |
| spellingShingle | Guenther Seufert Giovanni Manca Giorgio Matteucci Terenzio Zenone Lorenzo Busetto Roberto Colombo Michele Meroni Mirco Migliavacca Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model Sensors Gross Primary Production Phenology BIOME-BGC PROSPECT SAILH Poplar plantations |
| title | 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_short | 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 |
| topic | Gross Primary Production Phenology BIOME-BGC PROSPECT SAILH Poplar plantations |
| url | http://www.mdpi.com/1424-8220/9/2/922/ |
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