## 1 Introduction  
Observation and terrestrial biosphere modelling studies have demonstrated the strong impacts of extreme climatic events on terrestrial ecosystem carbon cycle [1; 2; 3]. The years of 2015/16 experienced one of the strongest El Nino events since the 1950s, which led to historic warm and dry conditions over the tropical region. This El Nino event has likely caused the historically high atmospheric CO\({}_{2}\) growth rate over 3 ppm year\({}^{-1}\) in 2015 and 2016 [4]. Subsequently, the historic warm and dry conditions resulted in widespread droughs globally, with greatest severity over regions including Amazon, central Europe, north China and Southeast Asia [5; 6; 7; 8]. Drought can have adverse impacts on both ecosystems and human society, such as regional agriculture and water resources. In Southeast Asia, severe drought associated with this monster El Nino event has caused a severe impact on crop production in Vietnam and Thailand [9]. In particular, the extended dry conditions in Indonesia led to widespread forest and peatland fires and largest carbon emissions in 2015 in the Southeast Asiasince 1997 [6]. The ocean-atmosphere events e.g., El-Nino and the Madden-Julian Oscillation (MJO), have substantial influence on extreme dry/wet conditions, which is associated with air temperature and precipitation [10]. Many efforts have been made to develop various quantitative measures for accurate monitoring of drought extent and severity [11]. Satellite observations of vegetation status, with its global coverage, provide useful information to monitor the spatiotemporal changes of vegetation in response to drought at broad scales, and thus improve simulations of carbon fluxes of terrestrial ecosystems. Since droughs are associated with vegetation state and coverage, greenness-based vegetation indices (VIs), such as normalized difference vegetation index (NDVI), have been widely used for drought monitoring [12]. Based on NDVI, several drought indices have been developed for regional to global scale drought assessment [11; 13]. Some other VIs, such as the enhanced vegetation index (EVI) [14], has been also widely used to monitor the vegetation response to drought in the Amazon [15] and in central Africa [16]. EVI has less saturation problems over dense forest area than other greenness index. However, the greenness-based VIs only indicates potential photosynthesis and relates to vegetation functioning indirectly. Furthermore, saturation of VIs in dense canopies of tropical forests leads to a lack of sensitivity to detect the stress-induced degradation in the forest canopy. Therefore, it has been suggested that greenness-based VIs are not sufficient to capture the dynamic response of vegetation to varying water stress [17]. In this regard, for stress detection, chlorophyll fluorescence (ChIF) is especially useful as a non-destructive probe, and has been used to investigate the plant response to water stress at leaf and canopy scales on the ground [18]. ChIF is an emission in the 650-800 nm range originating at the core of the photosynthetic machinery after light absorption [19], and the ChIF emission has been widely used as a signal on plant photochemical function. Global retrievals of solar-induced chlorophyll fluorescence (SIF) have become available over the recent years [20; 21; 22; 23]. Recent studies have shown that SIF is a good proxy for vegetation photosynthesis or gross primary productivity (GPP) [24; 25; 26; 27; 21]. Under drought conditions, satellite-based SIF data have also been used to monitor large-scale vegetation status in Amazon forest [15], Midwest US [29], Russia [30], and Australia [31]. These studies show that SIF provides unique, perhaps the most direct, spatial information for early warning and accurate monitoring of appearing drought. Southeast Asia has 2.1 million km\({}^{2}\) of forests, only accounting for 17% of the area of global tropical forests [32] but with 26% of the global tropical forest biomass carbon stock [33].