## 3 Results
### Temporal and Spatial Characteristics of GPP, SIF, and VIs  
We analyzed the spatial patterns of GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI anomalies during the drought period in 2009 (Figure 5). From Figure 5, it can be observed that GPP and SIF anomalies exhibit similar spatial patterns, indicating that SIF captures the variations in GPP. Negative anomalies in GPP and SIF are mainly distributed in the central and eastern parts of the Xilingol grassland in July and August, while most areas of the grassland show positive anomalies in September. Negative anomalies in SIF\({}_{\text{yield}}\) are mainly observed in the northeast and western regions of the grassland from April to August. Negative anomalies in NDVI are mainly observed in the northeast, central, and southern parts of the Xilingol grassland in June, July, and August, with a larger extent and severity compared to GPP  
Figure 4: Spatial distribution characteristics of anomalies of SPEI, SM, PPT, and LST in the Xilingol grassland from April to October 2009. The anomalies SPEI, SM, PPT, and LST were calculated by subtracting the pixel’s monthly value from the pixel’s average monthly value for 2007–2015. and SIF. However, the anomalies of \(\text{SIF}_{\text{yield}}\) show inconsistency towards SIF and GPP at most of the times and regions. To explain the inconsistency of SIF and \(\text{SIF}_{\text{yield}}\), the monthly anomalies of SIF, \(\text{SIF}_{\text{yield}}\), fPAR, PAR, and APAR were calculated by subtracting the monthly value of indicators from the average of each particular month through the 2007-2015 period. According to Equation (1), we know that SIF and \(\text{SIF}_{\text{yield}}\) are directly proportional to each other but are affected by APAR. When SIF increases, \(\text{SIF}_{\text{yield}}\) and SIF have the same trend only when APAR remains the same or increases less than the increase in SIF. As can be seen from Figure 6, SIF decreased by 126.04% in May compared to April, while APAR increased by 1427.75%, which leads to different trends in SIF and \(\text{SIF}_{\text{yield}}\). Also, in June, July, and August, the trend of APAR is either different from SIF or higher than that of SIF. This phenomenon causes the inconsistency between SIF and \(\text{SIF}_{\text{yield}}\). Figure 5: Spatial distribution characteristics of anomalies of GPP, SIF, \(\text{SIF}_{\text{yield}}\), and NDVI in the Xilingol grassland from April to October 2009. The monthly anomalies were calculated by subtracting the monthly value of indicators from the average of each particular month through the 2007–2015 period. Overall, GPP, SIF, and NDVI exhibit similar spatiotemporal patterns during the drought (Figure 5), with GPP and SIF showing closer spatiotemporal variations. These results suggest that satellite-based SIF can effectively monitor the dynamic progression of drought and accurately track the spatiotemporal changes in GPP under drought conditions. To further evaluate whether SIF can serve as an effective indicator for monitoring GPP, we conducted a statistical analysis by comparing the downscaled GOME-2 SIF values with the FLUXCOM GPP product. From Figure 7, it can be observed that SIF and GPP showed a significant correlation with R\({}^{2}\) of 0.95 (\(p<0.001\)). However, the correlations between NDVI, SIF\({}_{\text{yield}}\), and GPP were significantly lower, with R\({}^{2}\) values of 0.78 and 0.66, respectively. The decrease in the correlation between NDVI and GPP might be due to NDVI saturation when grass growth rate and coverage are high. The relationship between SIF\({}_{\text{yield}}\) and GPP was reduced because of the lack of information on vegetation canopy structure as SIF\({}_{\text{yield}}\) removed the information on fPAR, which represents the vegetation canopy structure information. Furthermore, the relationship of the anomalies of SIF, SIF\({}_{\text{yield}}\), and NDVI with GPP exhibited a similar pattern to the relationship between the measured values of these variables.