Skip to main content

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

Buy Article:

$105.00 plus tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: 2012-05-01

More about this publication?
  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more