Validation of MODIS gross primary productivity for a subtropical coniferous plantation in Southern China

Terrestrial carbon cycle plays an important role in global climate change. As a key component of terrestrial carbon cycle, gross primary productivity (GPP) is a major determinant of the exchange of carbon between the atmosphere and terrestrial ecosystems. 8-day global GPP estimated from ground meteorological data and remotely sensed fraction of photosynthetic active radiation (fPAR) by MODIS using the light use efficiency approach is currently provided as MOD17 product. Previous studies indicated that MODIS GPP has large uncertainties in some ecosystems. In this study, GPP of a subtropical coniferous plantation at Qianyanzhou Experimental Station in southern China was firstly calculated using the MODIS GPP algorithm (MOD17 algorithm) driven by MODIS fPAR and measured meteorological data. Calculated GPP was validated using GPP measured during 2003 and 2004 with the eddy covariance technique. Then the potential to better MODIS GPP was investigated through comparing GPP calculated using the MOD17 algorithm and improved fPAR or/and maximum light use efficiency (ε_max) calibrated with measured GPP. The results indicated that the MODIS GPP product significantly underestimated measured GPP at this planted forest. The R² of MODIS GPP with measured GPP was 0.72 and 0.67 in 2003 and 2004, respectively. And the calculated annual GPP was 33% and 47% lower than measured values in these two years. The improvement on fPAR through using LAI data estimated with photosynthetic active radiation (PAR) measured above and below canopy can definitely remedy underestimation of annual GPP. The application of ε_max determined through model calibration improved annual GPP more significantly, indicating that the errors in MODIS GPP at this site can be mainly attributed to the underestimation of fPAR and ε_max. When the improved fPAR and ε_max were used, the agreement between calculated and measured 8-day GPP imp- - roved significantly, with R² equals to 0.78 and 0.85 for years 2003 and 2004, respectively. And the calculated annual GPP was only 3.5% lower and 1.3% higher than measured values in these two years. Through this study, it can be concluded that accurate ε_max and LAI from which fPAR is calculated are required for reliably calculating regional/global GPP with the MOD17 algorithm. The fusion of flux data with remote sensing data can provide the accurate estimate of ε_max andhas a great potential to control uncertainties in calculated regional/global GPP.