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Keywords:

  • gross primary production;
  • eddy covariance;
  • light use efficiency;
  • validation;
  • MODIS

[1] Gross primary production (GPP) is a significant important parameter for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large-scale GPP estimation. GPP was estimated as a product of vegetation indices (VIs) and photosynthetically active radiation (PAR). Four kinds of vegetation indices [the normalized difference vegetation index (NDVI), the weighted difference vegetation index, the soil-adjusted vegetation index, and the enhanced vegetation index (EVI)] derived from the Moderate Resolution Imaging Spectroradiometer daily surface reflectance product were selected to test our method. The in situ GPP was calculated using the eddy covariance technique and the PAR data were acquired from meteorological measurements. Because VIs were found to be a reliable proxy of both light use efficiency (LUE) and the fraction of absorbed PAR (fAPAR; R2 of 0.63–0.87 for LUE and 0.69–0.76 for fAPAR), the product VI × VI × PAR is used as a measure of GPP according to Monteith logic. Moderate determination coefficients R2 from 0.65 for NDVI to 0.71 for EVI were obtained when GPP was estimated from a single index in maize. When testing our method, calculating GPP as a product of VI × VI × PAR, the determination coefficients R2 largely improved, fluctuating from 0.81 to 0.91. EVI × EVI × PAR provided the best estimation of GPP with the highest R2 of 0.91 because EVI was found to be the best indicator of both LUE and fAPAR (R2 of 0.87 and 0.76, respectively). These results will be helpful for the development of future GPP estimation models.