## 3 Results
### Capture of GPP by VIs and SIF in Dry Years
#### 3.4.2 Capture of GPP Changes by VIs and SIF  
To further analyze the ability of both to capture GPP loss during drought events, we examined the trends, expressed as a ratio of difference to mean (Figure 10). In sparse vegetation, GPP decreased by 16.19% in May while SIF decreased by more than ten times the multi-year mean, GPP decreased by 53.72% in June while SIF decreased by 179.19%, and GPP decreased by 47.39% in July while SIF decreased by 95.74%. In grassland, SIF changes were not as strong as in sparse vegetation, but SIF still showed the worst performance (except in June when it outperformed NDVI and NIR\({}_{\text{V}}\)). In cropland, probably due to anthropogenic influence, SIF started to capture GPP loss slightly better than NDVI. As the productivity of the land type increased, SIF gradually outperformed VIs. For example, in deciduous forests, the trend of SIF change in June and July was significantly closer to the trend of GPP change, followed by NIR\({}_{\text{V}}\). It can be seen that SIF does not perform as well as kNDVI and NIR\({}_{\text{V}}\) in areas with low productivity and low vegetation cover during drought events.  
Figure 10: Trends in VIs, SIF, and GPP in 2001. D/M is the ratio of the difference to the mean: (2001 value \(-\) mean)/mean value.  
We evaluated the magnitude and spatial distribution of the anomalies of VIs and SIF from May to July 2001 (Figure 11). From the spatial distribution of anomalies, VIs, and SIF were basically in moderate negative anomalies. Negative anomalies appeared in May, and strong negative anomalies of the three VIs were mainly distributed in cropland in Shaanxi and Shanxi. The SIF shows strong negative anomalies not only in the cropland but also in the grassland northwest of the Loess Plateau. In June, the negative anomalies are more extensive and strong, with strong negative anomalies in most areas of the Loess Plateau. In July, the negative anomalies are slightly reduced, with strong negative anomalies mainly in Inner Mongolia, Shaanxi, and Shanxi. Compared with the VIs, the negative anomalies of SIF are more serious, especially in June when SIF reaches strong negative anomalies in the forest-shrub-grass vegetation mosaic belt, deciduous forests, and evergreen forests. In general, the anomalies in the east were more severe than those in the west. A large part of the Gansu territory remained normal in these three months.  
To further understand the capture of anomalies by VIs and SIF, the relationship between anomalies of VIs and SIF and GPP anomalies was also explored (Figure 12). The capture of anomalies was best for kNDVI (meanR\({}^{2}\) = 0.50), followed by NIRV (meanR\({}^{2}\) = 0.37), NDVI (meanR\({}^{2}\) = 0.35), and SIF (meanR\({}^{2}\) = 0.26), which was the poorest performer among the four indices. Specifically, kNDVI was the best performer in all land classes except in sparse vegetation, where NIRV and NDVI outperformed kNDVI. Even though the other indices were very low in both deciduous and evergreen forests, kNDVI still had a high correlation NIRV (meanR\({}^{2}\) = 0.35 and 0.42). We found this to be a significant departure from the initial hypothesis (that SIF would perform better).  
Figure 11: Spatial distribution of anomalies in VIs and SIF for May, June, and July 2001.