Next Article in Journal
The Spectral Response of the Landsat-8 Operational Land Imager
Previous Article in Journal
Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(10), 10215-10231; doi:10.3390/rs61010215

Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data

1,2,†,* , 3,†
and
1,2
1
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 22 July 2014 / Revised: 20 October 2014 / Accepted: 21 October 2014 / Published: 23 October 2014
View Full-Text   |   Download PDF [1214 KB, uploaded 23 October 2014]   |  

Abstract

Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003–2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model’s application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances. View Full-Text
Keywords: gross primary production (GPP); MODIS; eddy covariance; model comparison; ChinaFLUX; land cover types gross primary production (GPP); MODIS; eddy covariance; model comparison; ChinaFLUX; land cover types
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, Z.; Wang, L.; Wang, S. Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data. Remote Sens. 2014, 6, 10215-10231.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top