## 1 Introduction  
Droughs have significant adverse impacts on vegetation carbon assimilation by increasing vegetation mortality rate and changing the species composition of local ecosystems [1; 2; 3]. Australia is particularly vulnerable to drought, as evident by the drought events in the 1980s, droughts in Victoria [4], 1990s Queensland drought [5], as well as the 'Millennium drought' between 2001 and 2009 [6]. Particularly, in 2009, southeastern Australia suffered high temperatures, in excess of 40\({}^{\circ}\)C, for several days in February [7]. The continuous high temperatures, combined with the drier conditions, further triggered bushfires which burned nearly 450,000 hectares of forest in Victoria [8]. Australia's high vulnerability to drought threatens regional carbon balance in the terrestrial ecosystem. Moreover, droughts are predicted to become more frequent and more intense, due to ongoing increases in the concentration of greenhouse gases [9; 10]. Hence, an accurate assessment of the impacts of drought on vegetation activity is crucial for understanding the response of terrestrial plants to climate anomalies, especially for drought-vulnerable regions, such as Australia. Among all the techniques, remote sensing plays an irreplaceable role in monitoring drought in a spatiotemporally-continuous manner. Traditional remotely sensed vegetation indices (VIs), including the Normalized Differences Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), have been used to monitor and analyze the spatiotemporal variation in drought-related vegetation activities [11; 12; 13]. For example, NDVI has been used as an indicator of drought-induced damage for terrestrial plants in southwest Australia [14]. NDVI has also been applied to build various extended indices to monitor drought, such as the vegetation condition index (VCI) [15] and the scaled drought condition index (SDCI) [16]. EVI, developed to improve the sensitivity over dense vegetation conditions [17], has been used to assess the drought impacts on Amazon forest canopies [18; 19]. VIs have always been used to indicate vegetation greenness and canopy structure [20]. In terms of reflecting impacts of environmental stress on vegetation, however, VIs are often reported to show delayed responses [21; 22]. This delay is because plant canopy and leaf greenness respond slowly to ambient stress that decreases the photosynthetic activity, especially in evergreen forest regions [22; 23]. During the process of photosynthesis, a part of the absorbed photosynthetically active radiation (APAR) by chlorophyll \(a\) pigments at 400-700 nm wavelengths is reemitted at longer wavelengths (660-800 nm) as fluorescence, which is referred to as solar-induced chlorophyll fluorescence (SIF) [24]. Since SIF is directly related to the photosynthesis, SIF is likely to respond rapidly to vegetation functioning changes induced by environmental stresses, such as water or heat stress [25; 26; 27; 28; 29]. Light use efficiency (LUE) model helps illustrate the relationship between gross primary production and SIF [30]. GPP is conceptually described by the LUE model as:  
\[\mathrm{GPP}=\mathrm{FPAR}\times\mathrm{PAR}\times\varepsilon_{\mathrm{P}} =\mathrm{APAR}\times\varepsilon_{\mathrm{P}} \tag{1}\]  
where FPAR represents the fraction of APAR versus photosynthetically active radiation (PAR), and \(\varepsilon_{\mathrm{P}}\) is the LUE that APAR is used for carbon assimilation by photosynthesis. In Equation (1), FPAR can be approximated from VIs [31], and \(\varepsilon_{\mathrm{P}}\) is assumed to be related to plant functional type and environmental stress conditions [32; 33].