Chin J Plant Ecol ›› 2016, Vol. 40 ›› Issue (1): 1-12.DOI: 10.17521/cjpe.2015.0253

Special Issue: 遥感生态学

• Orginal Article •     Next Articles

Remote sensing estimation of gross primary productivity and its response to climate change in the upstream of Heihe River Basin

YAN Min, LI Zeng-Yuan*, TIAN Xin, CHEN Er-Xue, GU Cheng-Yan   

  1. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
  • Online:2016-01-01 Published:2016-01-28
  • Contact: Zeng-Yuan LI
  • About author:# Co-first authors

Abstract: AimsQuantifying the gross primary productivity (GPP) of vegetation is of primary interest in studies of global carbon cycle. This study aims to optimize the MODIS GPP model for specific environments of a fragile waterhead ecosystem, by performing simulations of long-term (from 2001 to 2012) GPP with optimized MOD_17 model, and to analyze the response of GPP to the local climatic variations.Methods The original MODIS GPP products that underestimate GPP were validated against two years (2010-2011) of eddy covariance (EC) data at two sites (i.e. an alpine pasture site and a forest site, respectively) in the upstream of Heihe River Basin. Three comparative experiments were then conducted to analyze the effects of input parameters derived from three sources (i.e. meteorological, biome-specific, and fraction of absorbed photosynthetically active radiation (fPAR) parameters) on the model behavior. After refining the model-driven parameters, long-term GPPs of the study area were estimated using the optimized MOD_17 model, and the Least Absolute Deviation method was applied to analyze the partial correlations between interannual GPPs and climatic variables (temperature, precipitation and vapor pressure deficit (VPD)). Important findings The uncertainties in the original MODIS GPP products are attributable to biome-specific parameters, input data (e.g. meteorological and radiometry data) and vegetation maps. At the pasture site, the light use efficiency had the strongest impact on the GPP simulations. The refined fPAR calculated from the leaf area index (LAI) products of Global Land Surface Satellite (GLASS) greatly improved the GPP estimates, especially at the forest site. The GPPs from the optimized MOD_17 model well matched the EC data (R2 = 0.90, root mean squared error (RMSE) = 1.114 g C·m-2·d-1 at the alpine pasture site; R2 = 0.91, RMSE = 0.649 g C·m-2·d-1 at the forest site). The time series of GPPs displayed an up trend at an average rate of 9.58 g C·m-2·a-1 from 2001 to 2012. Examination of the partial correlations between interannual GPPs and climatic variables showed that the annual mean temperature and VPD generally had significant positive impacts on GPP, and the annual precipitation had a negative impact on GPP.

Key words: gross primary productivity, MOD_17 model, trend analysis, climatic factors, partial correlation analysis