## 5 Discussion
### Uncertainties in Updated Forest Aboveground Biomass  
When comparing forest AGBs obtained using the BEPS-based method with the one produced based on with field-based measurements, the discrepancy between them was distinguishable even if the determination coefficient between them was statistically at a significance level of 0.05. The BEPS-based AGB was mostly higher than field-based AGB for plots with low field-based AGB. In contrast, the BEPS-based AGB was mostly lower than field-based AGB for plots with high field-based AGB (Figure 7). Uncertainties in BEPS-based AGB results were mainly from two factors. The first factor was the initialization uncertainties of AGB in 2008, which was estimated using the AGB-LAI empirical model set up with field measurements recorded in 2012. As shown in Figure 9, AGB-LAI empirical model tended to underestimate or overestimate high or low AGB, respectively. The second one was the uncertainties in annual NPP simulated by the BEPS model, which was mainly driven by the LAI series generated by the MODIS and TM data. When estimating the remotely sensed-based LAI with the empirical relationship between field-based LAI and vegetation index calculating from remote sensing images, it would cause underestimation or overestimation of overstory LAI for dense or sparse forests [62], respectively. The underestimation of high LAI was mainly caused by the spectral saturation of optical remote sensing [62] while the overestimation of low AGB mainly resulted from the large contribution of understory vegetation to the reflected signal detected by the sensor [63]. The saturation of optical remote sensing signals and contribution of understory vegetation caused underestimation or overestimation of overstory LAI for dense or sparse forests, respectively, which induced annual NPP and ABI of overstory to be underestimated or overestimated for dense or sparse forests, respectively. Of course, uncertainties in field-based AGB also have influences on the agreement between BEPS-based and field-based AGB. Field-based dataset from 74 plots is not large enough, although we set up these plots with different density categories of forest canopy and different topography variations under the guidance of local forest explorers. These plots were distinguished by three classes (i.e., needle leaf forest, broadleaf forest, and mixed forests) when used for building models for LAI and AGB estimations. The limitation in the number of sampling plots would potentially induce some uncertainties in the trained statistical model. Undoubtedly, the accuracy of the field-based LAI and AGB results calculated from these models would be affected. Differences between the BEPS- and ALS/field-based AGBs could also be introduced from other three aspects, including statistical model, landcover maps and forest ages. Due to the lack of field measurements in 2008, AGB in 2008 was estimated from remotely sensed LAI with the statistical model trained using field measurements of AGB and LAI taken in 2012 and 2013. This simple temporal extrapolation of the statistical model might induce uncertainness in the baseline forest AGB of 2008, which will be propagated into updated BEPS-based AGB. Additionally, errors were also brought from landcover maps. (1)The Landsat ETM+ image-based forest cover maps only for 2008 and 2012 were produced to characterize the forest dynamic changes, which might not adequately capture the annual variations of forest types between neighboring years. (2) The different land surface objects of a forest stand measured by remote sensors and field measurements work may introduce errors. The information collected by MODIS or Landsat TM/ETM+ contained the spectral responses of vertical structure of a forest stand including overstory (i.e., live standing trees), understory (i.e., shrubs, grass) [64], bare soil ground, and the shadows between the forest canopies. However, the forest AGB measured by field work only concerned the live standing trees with DBH >5 cm.