## 4 Results
### Monitoring and Assessing the 2009 Drought in Victoria
In Table 1, memory effects to GPP and SIF cannot be ignored compared to meteorology stress. It is interpreted that anomalies in vegetation productivity might result from either past meteorology anomalies or current stress conditions [51]. The values of \(\text{a}^{\text{M}}\) and \(\text{a}^{\text{T}}\) induced by APAR\({}_{\text{NDVI}}\) are similar for general Victoria region and crop land. These results are consistent with the results of GPP and SIF. \(\text{a}^{\text{W}}\) estimated by GPP and SIF in crop land are higher than in evergreen broadleaf forest land. It indicates that the sensitivity of crops to water availability is stronger than that of evergreen broadleaf forest. This result might be related to the different resistances to water availability between crop and forest ecosystems. Forest ecosystems have deeper roots and a higher capability to utilize soil water compared with crop ecosystems [11]. Therefore, forest ecosystems are more resistant to water stress than are crop ecosystems. In general, SIF anomaly can be explained by the combined stress from temperature, water availability and PAR for general Victoria region and crop land. The sensitivity of SIF to meteorology anomalies provides its potential superiority for monitoring vegetation productivity variations accurately under drought conditions. SIF anomaly showed a stronger linear relationship with GPP anomaly than did APAR anomalies (Figure 7). This result may be related to the relationship between \(\epsilon_{\text{P}}\) and \(\epsilon_{\text{F}}\). [PERSON] et al. [41] reported a strong linear relationship between \(\epsilon_{\text{F}}\) and \(\epsilon_{\text{P}}\). According to Equations (1) and (2), although both SIF and APAR are responsive to FPAR, which is related to chlorophyll content and LAI, SIF, but not APAR, is sensitive to variations in \(\epsilon_{\text{F}}\). Therefore, GPP anomaly has a better consistency with SIF anomaly than with APAR anomalies. SIF was retrieved using a simplified radiative transfer model together with a principal component (PC) analysis to disentangle the spectral signatures of atmospheric absorption, surface reflectance and fluorescence emission. [42; 43]. Previous studies have discussed the effect of the fit window, the numbers of PCs, and the reference area for PC determination on the retrieval outcome [42; 43; 55]. [PERSON] et al. depicted the standard error of the mean of GOME-2 SIF composites for January 2011, and the results showed that the standard error of the mean was generally low (\(<\)0.15 mW/m\({}^{2}\)/sr/nm) for GOME-2 SIF in Australia [42]. The coarse-spatial- and low-temporal-resolution data source for SIF can constrain its potential. The feasibility of SIF should be further investigated using data with a higher spatio-temporal resolution in the future [56]. It has been reported that FLUXCOM GPP data contain uncertainty regarding the evergreen broadleaf forest land (especially in the Amazon and Indonesia) [44]. To evaluate the quality of the regional GPP product used in this study, especially for evergreen broadleaf forest land in Victoria, we validated FLUXCOM GPP against eddy-covariance-based GPP estimations (Appendix B Figure A1). The results indicate that the change tendency of FLUXCOM GPP is similar to that of flux tower-derived GPP. Overall, FLUXCOM GPP can be used to investigate the effects of meteorology anomalies on vegetation productivity [45] and capture the considerable drawdown of vegetation activities during the 2009 drought event in Victoria.