Open AccessThis article is
- freely available
Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model
Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Milano, Italy
Institute on Atmospheric Pollution (IIA), National Research Council (CNR), Roma, Italy
Department of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy
European Commission, DG-JRC, Institute for Environment and Sustainability, Climate Change Unit, Ispra (VA), Italy
Institute for Mediterranean Agricultural and Forest Systems, National Research Council, Rende (CS), Italy
* Author to whom correspondence should be addressed.
Received: 6 October 2008; in revised form: 10 February 2009 / Accepted: 12 February 2009 / Published: 13 February 2009
Abstract: 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.
Keywords: Gross Primary Production; Phenology; BIOME-BGC; PROSPECT; SAILH; Poplar plantations
Citations to this Article
Cite This Article
MDPI and ACS Style
Migliavacca, M.; Meroni, M.; Busetto, L.; Colombo, R.; Zenone, T.; Matteucci, G.; Manca, G.; Seufert, G. Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model. Sensors 2009, 9, 922-942.
Migliavacca M, Meroni M, Busetto L, Colombo R, Zenone T, Matteucci G, Manca G, Seufert G. Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model. Sensors. 2009; 9(2):922-942.
Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, Roberto; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, Guenther. 2009. "Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model." Sensors 9, no. 2: 922-942.