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Estimation of light use efficiency by using remote sensing data

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center of Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875,China

  • Email：201721170059@mail.bnu.edu.cn
  • Introduction：朱安然，1995年生，女，硕士研究生，研究方向为光能利用率估算。E-mail：201721170059@mail.bnu.edu.cn
ZHU Anran12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center of Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875,China

  • Email：sunrui@bnu.edu.cn
  • Introduction：孙睿，1970年生，男，教授，研究方向为植被生产力。E-mail： sunrui@bnu.edu.cn
SUN Rui12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center of Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875,China

WANG Mengjia12

Resümee

Light Use Efficiency (LUE) characterizes the efficiency with which vegetation converts intercepted or absorbed solar radiation into organic dry matter through photosynthesis. LUE is a key parameter to estimate vegetation productivity, especially in the widely used methods of global or regional Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) estimation with remote sensing data, such as MODIS vegetation productivity algorithm (MOD17), the Carnegie-Ames-Stanford Approach (CASA), the Vegetation Photosynthesis Model (VPM) and the Eddy Covariance-Light Use Efficiency approach (EC-LUE). In these methods, LUE was estimated by multiplying temperature, water and other stress factors with maximum LUE at optimal environment conditions. Due to the combined effects of vegetation type and climate, LUE shows significant spatial heterogeneity and seasonal variation. The uncertainty of LUE estimation is an important fact for the low accuracy of subsequent productivity models. It is necessary and significant to improve the accuracy of LUE estimation.Based on global Fluxnet site data and MODIS LAI (Leaf Area Index)/fPAR (Fraction of Photosynthetic Active Radiation) products, this paper compared five existing LUE estimating methods (MOD17, CASA, Dry Matter Productivity algorithm of Global Monitoring for Environment and Security (GME DMP), VPM, EC-LUE) first. And in order to reflect the difference of LUE between the sunlit and shaded leaves, we took the Clearness Index (CI, the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere) into account and established two LUE estimation models by stepwise linear regression method and parameter optimization method respectively. In the parameter optimization method, we first determined the maximum LUE of the sunlit and shaded leaves for each type of vegetation by an optimization algorithm developed at the University of Arizona (SCE-UA), and then we estimated the actual LUE by adjusting the maximum LUE with CI, temperature and water stress factors.The comparison of five existing LUE estimation methods shows that the R2 between estimated LUE and fluxnet LUE is within 0.028—0.282, and the RMSE range is 0.545—0.681 gC·MJ-1. The MOD17 method has the lowest RMSE at 0.545 gC·MJ-1, followed by EC-LUE with 0.579 gC·MJ-1. While R2 is highest for EC-LUE (R2=0.282), and followed by CASA (R2=0.185), which is related to the factor of evaporation fraction (EF) adopted by both methods. The correlation coefficient between EF and LUE is higher than other factors. On the whole, EC-LUE performs best (R2=0.282, RMSE=0.579 gC·MJ-1) among these five methods.The validation results show that the inclusion of Clearness Index can improve the accuracy of LUE estimation, and the RMSE by the stepwise linear regression method and parameter optimization method are both less than 0.5 gC·MJ-1. Although the stepwise linear regression method lacks the mechanism, the estimation accuracy of LUE (R2=0.461, RMSE=0.403 gC·MJ-1) is higher due to the more selected factors. The parameterization method has a slightly lower accuracy (R2=0.306 , RMSE=0.489 gC·MJ-1) due to fewer factors and a relatively fixed model form. The LUE estimation models established in this paper can be used for the estimation of regional or global LUE and vegetation productivity.

Schlüsselwort

light use efficiency;stepwise linear regression;parameter optimization;remote sensing;clearness index;gross primary production

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