Calibration of MODIS-based gross primary production over an alpine meadow on the Tibetan Plateau

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Moderate-resolution imaging spectroradiometer (MODIS) gross primary production (GPP) was compared with estimated GPP (GPP_EC) from eddy covariance measurements over an alpine meadow on the Tibetan Plateau in 2005–2007. The MODIS GPP (GPP_MOD17A2) with a bias of −0.38 g C m−2 d−1 (i.e., about −40.58% of the mean of the GPP_EC) strongly underestimated the GPP_EC for the alpine meadow. The MODIS GPP was recalibrated using measured surface meteorological data, including photosynthetically active radiation (PAR), daily minimum air temperature (Tamin) and daytime mean vapor pressure deficit (VPD), revised fractional photosynthetically active radiation (FPAR), and the revised maximum light use efficiency (LUEmax) of 0.81 g C MJ−1 (compared with the default value of 0.68 g C MJ−1 for grassland in the MODIS GPP algorithm) for the alpine meadow. The MODIS-based FPAR was about 14.70% larger than the surface-estimated FPAR using surface-measured leaf area index (LAI) data. Additionally, the temporal resolution of surface-measured LAI data was relatively low. Therefore, the linear relationship between surface-measured LAI and MODIS-based LAI was established (R2 > 0.80, P < 0.001). Then the revised MODIS LAI datasets were used to calculate the revised FPAR. The revised LUEmax was optimized from the MOD17A2 algorithm using daily surface measurements, including LAI, PAR, VPD, Tamin and GPP_EC. The calibrated MOD17A2 algorithm could explain 88% of GPP_EC variance for the alpine meadow. The bias between GPP_MOD17A2 and calculated GPP from the MOD17A2 algorithm using surface-measured PAR, Tamin, and VPD, MODIS-based FPAR, and the default LUEmax of 0.68 g C MJ−1 was −0.17 g C m−2 d−1 (i.e., about −17.60% of the mean of the GPP_EC). The underestimation of LUEmax caused a 13.78% underestimation of GPP. In contrast, the overestimation of FPAR resulted in a 7.17% overestimation of GPP. The net effect of meteorology data and FPAR resulted in a 13.84% underestimation of GPP. These results showed that MODIS-based meteorology data, FPAR, and LUEmax for the alpine meadow needed to be adjusted.