Estimating the gross primary productivity based on VPM correction model for Xishuangbanna tropical seasonal rainforest

Due to the unique climatic characteristics and vegetation features of tropical regions, the correlation R-2 of gross primary productivity (GPP) estimation in tropical regions using remote sensing models was generally lower than 0.3. Therefore, for the cloudy and rainy tropical regions, the influence brought by clouds on remote sensing images needed to be considered in GPP estimation. This paper developed a corrected vegetation photosynthesis model (VPM) for estimating GPP under cloudy conditions. It mainly corrected the two parameters, W-scalar and Enhanced Vegetation Index (EVI), which were obtained from remote sensing images and were therefore greatly influenced by clouds in the model. First, the water stress factor W-scalar was replaced by Evaporation Fraction (EF). Secondly, using the good correlation between near surface temperature and EVI, the conversion coefficient between near surface temperature and EVI was fitted to achieve the effective reconstruction of EVI contaminated by clouds. The correction of the two factors improved the estimation accuracy of the VPM model, and the comparison with the observed values of the GPP site in 4 years showed that the correction of EVI had a better improvement, with an increase of 0.22 in R-2 compared with the pre-correction, and the correction of W-scalar was increased by 0.11 in R-2. To verify the proposed method, the in-situ observation data of Xishuangbanna flux site from 2007 to 2010 were used. The results showed that the proposed method effectively improved the accuracy of GPP estimation by VPM model, especially in 2007 it was strongly influenced by clouds, and the improvement was significant, with R-2 increasing from 0.2 to 0.82. In general, the accuracy of GPP estimation by the proposed method had been significantly improved, with RMSE (gC center dot m(-2)center dot 8 day(-1)) decreasing from 15,14.4, 18.1, 14.2 to 8.07, 6.56, 10.33, 11.44, respectively. Therefore, the proposed method can be used to estimate the GPP for tropical seasonal rain forests in Xishuangbanna.