## 1 Introduction
Because SIF is also originated from APAR, correspondingly, SIF can be expressed as:  
\[\mathrm{SIF}=\mathrm{FPAR}\times\mathrm{PAR}\times\varepsilon_{\mathrm{F}} \times\mathrm{f}_{\mathrm{esc}}=\mathrm{APAR}\times\varepsilon_{\mathrm{F}} \times\mathrm{f}_{\mathrm{esc}} \tag{2}\]  
where \(\varepsilon_{\mathrm{F}}\) refers to the quantum yield for fluorescence and \(\mathrm{f}_{\mathrm{esc}}\) accounts for the fraction of SIF photons escaping from the canopy [34; 35]. \(\mathrm{f}_{\mathrm{esc}}\) can be assumed to be relatively constant for a given vegetation type [30]. Therefore, \(\varepsilon_{\mathrm{F}}\) gives SIF more potential to represent GPP beyond APAR. The drought has two direct impacts on vegetation photosynthesis process [36]. On one hand, drought affects plant physiological functions, such as reductions in enzyme activity and stomatal conductance, which can in turn slow down \(\varepsilon_{\mathrm{P}}\) and \(\varepsilon_{\mathrm{F}}\)[37; 38]. On the other hand, severe drought results in changes in vegetation greenness and canopy structure (e.g., loss of chlorophyll and leaf wilting), which can be reflected by changes in VIs [11][PERSON] These changes cause a reduction in FPAR and therefore may induce a suppression in APAR. These two drought impacts take effects at different time scales: plant physiological functions respond at the scale of minutes to days, while vegetation greenness and canopy structure respond at the scale of days to weeks [11][PERSON] Thus, there may be a lagged response of greenness-related VIs, which can only be used to estimate APAR, to changes in GPP induced by drought stress [22]. For SIF, the consequence of these two drought impacts on carbon assimilation can be reductions in \(\varepsilon_{\text{F}}\) and the amount of APAR and therefore a decline in SIF. SIF is therefore expected to respond rapidly to drought and indicate drought-induced reductions of vegetation production. For example, [PERSON] et al. [39] found SIF was more sensitive to short-term standard precipitation index (SPI) than was NDVI. [PERSON] et al. [26] reported that SIF and GPP reductions, due to droughts in croplands and grasslands resulted from declines in FPAR, \(\varepsilon_{\text{F}}\) and \(\varepsilon_{\text{P}}\). However, compared with APAR, the superiority of SIF to monitor drought-induced variations in GPP is still uncertain, because the relationship between \(\varepsilon_{\text{F}}\) and \(\varepsilon_{\text{P}}\) remains unclear [40; 41]. Besides, the spatiotemporal patterns of drought monitored by SIF and APAR estimations based on VIs have not been fully investigated and discussed among different vegetation types. In addition, the comprehensive quantification of the susceptibility of SIF to meteorology anomalies is unclear in drought vulnerable area. Last but not least, spatiotemporal aggregation is often needed for satellite SIF retrievals prior to analyses, due to retrieval noise [27; 42]. Whether there is an earlier manifestation of SIF losses, due to drought compared to APAR at a relatively coarse temporal resolution still remains a question. In this study, we evaluate the feasibility of SIF to monitor drought effects on vegetation productivity and compare the results with APAR estimations based on VIs for the main vegetation types (crop and evergreen broadleaf forest) in Victoria. The major objectives of this study are to (1) explore whether SIF possesses obvious superiority to monitor drought-induced variations in GPP compared with APAR, (2) better understand the potential advantage of SIF over APAR for monitoring drought-related GPP dynamics regarding different vegetation types, and (3) investigate the sensitivities of GPP and vegetation proxies (SIF and APAR) to different meteorology anomalies.