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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 368
Proc. IAHS, 368, 75–80, 2015
https://doi.org/10.5194/piahs-368-75-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 368, 75–80, 2015
https://doi.org/10.5194/piahs-368-75-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2015

06 May 2015

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

S. Wang1,2 and X. Mo1 S. Wang and X. Mo
  • 1Key Laboratory of Water Cycle and Related Land surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • 2University of Chinese Academy of Sciences, Beijing, 100049, China

Keywords: Gross primary production (GPP), VPM, GR, TG, cropland

Abstract. 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.

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