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Comparison of Multiple Models for Estimating Gross Primary Production Using Remote Sensing Data and Fluxnet Observations : Volume 368, Issue 368 (06/05/2015)

By Wang, S.

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Book Id: WPLBN0004016190
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Comparison of Multiple Models for Estimating Gross Primary Production Using Remote Sensing Data and Fluxnet Observations : Volume 368, Issue 368 (06/05/2015)  
Author: Wang, S.
Volume: Vol. 368, Issue 368
Language: English
Subject: Science, Proceedings, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Mo, X., & Wang, S. (2015). Comparison of Multiple Models for Estimating Gross Primary Production Using Remote Sensing Data and Fluxnet Observations : Volume 368, Issue 368 (06/05/2015). Retrieved from http://www.ebooklibrary.org/


Description
Description: Key Laboratory of Water Cycle and Related Land surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. In this study, gross primary production (GPP) estimated from a temperature and greenness (TG) model, a greenness and radiation (GR) model, a vegetation photosynthesis model (VPM), and a MODIS product have been compared with eddy covariance measurements in cropland during 2003–2005. Results showed that the determination coefficients (R2) between fluxnet GPP and estimated GPP were all greater than 0.74, indicating that all these models offered reliable estimates of GPP. We also found that the VPM-based GPP estimates performed a bit better (R2 is 0.82, and RMSE is 16.75 gC m−2 (8 day)−1) than other models, mainly due to its comprehensive consideration of the stresses from light, temperature and water. The actual GPP was overestimated in the non-growing season and underestimated in the growing season by MOD_GPP. The validation confirms that the above three models may be used to estimate crop production in the North China Plain, but there are still significant uncertainties.

Summary
Comparison of multiple models for estimating gross primary production using remote sensing data and fluxnet observations

 

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