## 3 Results  
### Interannual Variations in Sugarcane Yield/GPP and EVI/SIF  
We first analyze the interannual variations in sugarcane yield and the mean EVI and SIF during the critical growth periods in the entire study area (Figure 2a). We found that the mean EVI and SIF showed a significant increasing trend from 2001-2020, and the sugarcane yield also increased significantly from 2004-2020. The mean SIF is significantly and highly correlated with sugarcane yield (R\({}^{2}\) = 0.76, \(p\) < 0.01), which indicates that SIF can explain approximately 76% of the interannual variations in sugarcane yield. The correlation of the EVI and yield (R\({}^{2}\) = 0.67, \(p\) < 0.01) was lower than that of SIF and yield. The results show that the capability of SIF to capture the interannual variations in sugarcane yield is better than that of the EVI. To further validate the results, we compared the correlations of the mean EVI, SIF, and GPP during the critical growth periods of sugarcane at the regional and county levels. We found that SIF (EVI) explained approximately 83% (79%) of the interannual variations in sugarcane GPP in the entire study area (Figure 2b,c). The relationships of sugarcane SIF and GPP are stronger than those of sugarcane EVI and GPP in all counties (Figure 3). For example, SIF can capture approximately 81% of the interannual variations in sugarcane GPP, whereas this value is 58% for EVI in Daxin County. This is because the EVI tends to  
Figure 2: The relationships between yield/GPP and mean EVI/SIF during the critical growth periods of sugarcane. (**a**) Interannual variations in yield, EVI, and SIF in the study area. (**b**,**c**) Scatter plots of GPP against mean EVI and SIF during the critical growth periods in the entire study area. R\({}^{2}\) and \(p\) indicate the linear correlations. underestimate (overestimate) the GPP for high (low) GPP ranges, leading to a larger error in approximating sugarcane GPP. All of these results indicate that the capability of SIF to capture the interannual variations in sugarcane GPP is better than that of the EVI at both the regional scale and county level. Figure 3: Scatter plots of GPP against mean EVI and SIF during the critical growth periods in (**a1,a2**) Longzhou, (**b1,b2**) Daxin, (**c1,c2**) Jiangzhou, (**d1,d2**) Fusui, and (**e1,e2**) Ningming Counties. Spatiotemporal Dynamics of SPEI, Precipitation, and Air Temperature during the Sugarcane Growth Period  
We use the seasonal variations in the anomalies of SPEI, precipitation, and air temperature to analyze the temporal dynamics of drought in the entire study area during the critical growth period of sugarcane (Figure 4). We find that the SPEI is less than \(-1\) from August to November 2009, and is also much lower than the corresponding values in all other years from August to November. In particular, the SPEI was smaller than \(-2\) in September 2009. These results indicate that the study area experienced the most severe drought in 2009 in recent years. Thus, we take the 2009 drought as a case study. The reasons for the 2009 drought event are mainly attributed to significantly reduced precipitation (Figure 4b) and higher air temperature (Figure 4c). Precipitation was approximately 100 mm lower than the multiyear mean, and air temperature was approximately 2.5 \({}^{\circ}\)C higher than the multiyear mean in August and September 2009. Figure 4: Seasonal variations in the anomalies of SPEI (**a**), precipitation (**b**), and air temperature (**c**) in the entire study area. The blue curves represent the monthly multiyear mean of each variable, and the red curves indicate the seasonal cycles of each variable in 2009. The grey curves in (**a**) represent the SPEI values from 2001 to 2018 except for 2009. The light green shadings indicate the 10 th–90 th percentile ranges of each variable in all years.