Abstract:Remote sensing is an effective method to assess terrestrial vegetation photosynthetic physiology and productivity dynamics at a regional scale. The conventional spectral vegetation index such as normalized difference vegetation index does not accurately reveal the photosynthetic phenology of subtropical evergreen forests because canopy structure is relatively stable across seasons. This study calculated the conventional canopy structural vegetation index (normalized difference vegetation index, NDVI), photosynthetic physiological and biochemical vegetation index (chlorophyll/carotenoid index, CCI), and chlorophyll fluorescence vegetation index (normalized difference fluorescence indices, NDFI) respectively, using the spectral reflection data from the automated multi-angular spectro-radiometer at the Dinghu Mountain Forest Ecosystem Research Station in Guangdong, China. We compared and analyzed their differences in tracking gross primary productivity (GPP) as measured by eddy covariance at the canopy level. A multivariate linear regression model was built to improve the fitting accuracy of GPP seasonal dynamics in this subtropical evergreen forest. The results show:for this mixed subtropical evergreen forest, 1) GPP was significantly correlated with all three indices, and the correlation with NDVI was the strongest (R2=0.60, P < 0.01); 2) CCI could not replace NDVI as a better vegetation index to reveal GPP seasonal dynamics (R2=0.55, P < 0.01); 3) NDFI could be used as a secondary index to effectively improve assessment of photosynthetic phenology (R2=0.68,P < 0.001).
