## 4 Results
### Spatial and Temporal Comparison among NPP Values  
To validate the simulated NPP, two verification methods are used in the study: field observation, which is the most convincing method; and cross-validation between the relevant models, especially with the MODIS NPP product MOD17 correlation between the comparison. As we all know, measured NPP data for large areas are not available; however, vegetation productivity and biomass are strongly correlated with NPP [35]. To validate the simulated NPP, the relationship between aboveground biomass and NPP can be applied. Biomass was measured in the field at 52 plots in September 2013 (Figure 7A). Field surveys were conducted on arbors (Figure 7B), shrubs (Figure 7C), and herbs (Figure 7D), with 23 arbor samples, 21 shrub samples, and eight herb samples. The plots were divided into four 5 m \(\times\) 5 m plots, and the two plots of the diagonal were selected to investigate the shrub layer. The herbaceous community randomly set a 1 m \(\times\) 1 m small plot in the grassland for the herbal investigation. The latitude and longitude of the survey plots and environmental factors such as slope, slope direction, soil type, and vegetation restoration time were recorded. The position of each field sample is measured by GPS, and the pixels of vegetation in the sample are pure when it is selected, so that the sample of the verification point and the pixel on the remote sensing image correspond. The ratio of belowground NPP (BNPP) to aboveground NPP (ANPP) was calculated using the following equations. Figure 5: NPP monthly changes trend from 2001 to 2011. Figure 6: The change rate of monthly NPP from the year 2001 to 2011. \[\text{ratio}=\frac{\text{BNPP}}{\text{ANPP}} \tag{31}\]  
\[\text{BNPP}=\text{BGB}\times\left(\frac{\text{nGB}}{\text{BGB}}\right)\times\text {turnover} \tag{32}\]  
\[\text{turnover}=0.0009\times\text{ANPP}+\ 0.25 \tag{33}\]  
where BGB is totally accumulated by belowground biomass (BGB), nBGB refers to the BGB for a current year, and turnover is the vegetation root turnover [36]. BGB and ANPP are provided by field measurements. We set liveBGB/BGB to 0.79 based on a field study by [37]. BNPP/ANPP ratios were 9.75, 7.25, and 3.29 in arbor, shrub, and herb communities, respectively. Dry weight of biomass was converted to weight of carbon using carbon content. Carbon content was considered 45% for aboveground biomass [38; 39]. In a comparison with field data, the correlation between measured NPP and estimated NPP was 0.51 (Figure 8), thereby showing a significant linear relationship between measured NPP and simulated NPP (p < 0.01). Figure 7: Distribution of plots in Zhongliang Mountain. By looking through the results of previous studies in the literature, we obtain the NPP values of the different vegetation types in the whole country and Chongqing area. The NPP values of other related NPP models were obtained during the same period (Table 5), and the applicability of GLOPEM-CEVSA model in Chongqing area was compared and analyzed. The average NPP of Chongqing per unit area during 11 years was 534.54 gC\(\cdot\)m\({}^{-2}\). This situation occurs because most of Chongqing's districts are located in the Three Gorges reservoir area, which has high forest coverage and is well-protected. The results of this study lie between those of the improved CASA model (R\({}^{2}\) = 0.756, Figure 9A) and the CEVSA model, which is close to the estimation result of Dong Dan, in which the vegetation characteristics of karst area are considered. The correlation between estimated NPP and MOD17 NPP was 0.56 (Figure 9B), indicating that the model implies a high accuracy. The spatial variation trend is consistent, but the NPP of evergreen coniferous forest was slightly higher. Miami and Thorthwait models are more accurate for simulating grass NPP, which is similar to the result of study.