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

Sensors

Mirco Migliavacca⁴  Michele Meroni⁴  Lorenzo Busetto²  Roberto Colombo⁴  Terenzio Zenone¹  Giorgio Matteucci³  Giovanni Manca³ 

[1] Department of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy;Institute on Atmospheric Pollution (IIA), National Research Council (CNR), Roma, Italy;European Commission, DG-JRC, Institute for Environment and Sustainability, Climate Change Unit, Ispra (VA), Italy;Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Milano, Italy

关键词: Gross Primary Production;    Phenology;    BIOME-BGC;    PROSPECT;    SAILH;    Poplar plantations;   

DOI  :  10.3390/s90200922

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.