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Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models

Author

Listed:
  • Jun Zhang

    (School of Environment and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730000, China
    Gansu Academy of Eco-Environmental Sciences, Lanzhou 730000, China)

  • Xufeng Wang

    (Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

  • Jun Ren

    (School of Environment and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730000, China)

Abstract

Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines the carbon that enters the ecosystem. The remote sensing-based light use efficiency (LUE) model is one of the primary tools that is currently used to estimate the GPP at the regional scale. Many remote sensing-based GPP models have been developed in the last several decades, and these models have been well evaluated at some sites. However, an accurate estimation of the GPP remains challenging work using LUE models because of uncertainties in the model caused by model parameters, model forcing, and vegetation spatial heterogeneity. In this study, five widely used LUE models, Glo-PEM, VPM, EC-LUE, the MODIS GPP algorithm, and C-fix, were selected to simulate the GPP of the Heihe River Basin forced using in situ measurements. A multiple-model averaging method, Bayesian model averaging (BMA), was used to combine the five models to obtain a more reliable GPP estimation. The BMA was trained using carbon flux data from five eddy covariance towers located at dominant vegetation types in the study area. Generally, the BMA method performed better than any single LUE model. From the case study in the study area, it is indicated that the trained BMA is an efficient method to combine multiple LUE models and can improve the GPP simulation accuracy.

Suggested Citation

  • Jun Zhang & Xufeng Wang & Jun Ren, 2021. "Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models," Land, MDPI, vol. 10(3), pages 1-10, March.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:3:p:329-:d:522291
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    References listed on IDEAS

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    1. Ashley Ballantyne & William Smith & William Anderegg & Pekka Kauppi & Jorge Sarmiento & Pieter Tans & Elena Shevliakova & Yude Pan & Benjamin Poulter & Alessandro Anav & Pierre Friedlingstein & Richar, 2017. "Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration," Nature Climate Change, Nature, vol. 7(2), pages 148-152, February.
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    Cited by:

    1. Panxing He & Yiyan Zeng & Ningfei Wang & Zhiming Han & Xiaoyu Meng & Tong Dong & Xiaoliang Ma & Shangqian Ma & Jun Ma & Zongjiu Sun, 2023. "Early Evidence That Soil Dryness Causes Widespread Decline in Grassland Productivity in China," Land, MDPI, vol. 12(2), pages 1-17, February.

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