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Parameters Optimization of Remote Sensing Drived Vegetation Gross Primary Production Model Using Ground Flux Measurement

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Abstract:

Gross primary production (GPP) is the rate of carbon fixation or gross assimilation per unit ground surface area. In general, there are two common approaches for GPP estimation: model based estimation and filed flux measurement. However, there may be patchy, and have gaps or biases using one of the methods. This study combines filed measurement with remote sensing drived GPP model, and explore the parameters optimization using the ground flux measurements. Region Production Efficiency Model (REG-PEM) is adopted in this study. 8-day GPP is calculated using REG-PEM model based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Total Ozone Mapping Spectrometer (TOMS) ultraviolet reflectance data at Qianyanzhou station in Jiangxi province, south of China in 2003 and 2004. Comparison between field measurements and model estimation suggest that GPP from flux data are greater than REG-PEM model estimation. Correlation coefficient between flux GPP and model GPP is 0.889. In order to improve accuracy of model estimated GPP, GPP calculated from flux data at Qianyanzhou station is assimilated to REG-PEM and the model parameters are optimized. The optimization is implemented by PEST software using Gauss-Marquardt-Levenberg algorithm. Optimized parameters include carbon dioxide concentration in inner leaf, the optimum air temperature, linear parameter and uncertainty parameter.

Keywords: FLUX; GROSS PRIMARY PRODUCTION; PARAMETER OPTIMIZATION; REMOTE SENSING

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/sl.2012.2290

Publication date: May 1, 2012

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