## 3 Results
### Response of Vegetation to Drought  
In this part, how vegetation productivity responded to this drought event were investigated in the crop and forest regions. Figure 4 showed the spatial distributions of normalized anomalies of EVI, SIF and \(\text{SIF}_{\text{yield}}\) in 2015 and 2016. For cropland, the spatial patterns presented by EVI anomalies showed an increase compared to the multiyear mean in most of cropland both in 2015 and 2016,  
Figure 3: The spatial distribution of normalized anomalies of (**a,b**) temperature, (**c,d**) precipitation, (**e,f**) radiation and (**g,h**) scPDSI in 2015 and 2016 relative to the means over the period 2007–2014 in Southeast Asia. SIF had positive anomalies during the early stage of the drought, but turned to negative pattern at the end of the drought. For the SIF\({}_{\text{yield}}\), substantial SIF\({}_{\text{yield}}\) reduced throughout cropland in both years, with 41.81% and 38.42% of severe losses (\(<\)1\(\sigma\)) in 2015 and 2016, respectively (Figure 4). For EBF region, it had very similar results with that from the cropland. The satellite observations of SIF\({}_{\text{yield}}\) showed a response to this drought with a reduction of approximately 60.54% in 2016. However, the EVI and SIF showed slight increases in 2016, which was not able to capture the negative response to the drought. Figure 5 showed the probability density of the spatial standard anomalies for the drought years. In cropland areas, the largest probability density of cropland precipitation in 2015 shifted left dramatically and it was slightly less than 0 in 2016. Meanwhile, the SIF\({}_{\text{yield}}\) shifted left obviously for both years, which indicated that negative SIF\({}_{\text{yield}}\) anomalies were existed in most of the study area. The largest probability density of SIF shifted left slightly in 2015 and significantly in 2016. However, the largest probability density of cropland EVI did not indicate any drought signal in 2015 and 2016. For the EBF area, the changes in maximum probability density of these factor anomalies were broadly similar, moving slightly left in 2015 and recovering in 2016, which indicated moderate drought  
Figure 4: The spatial distribution of normalized anomalies of (**a,d**) EVI, (**b,e**) SIF, (**c,f**) SIF\({}_{\text{yield}}\) in 2015 (left column) and 2016 (right column) relative to the means over the period 2007–2014 in Southeast Asia. condition in 2015. Overall, the spatial analysis demonstrated that satellite \(\text{SIF}_{\text{yield}}\) available from current instruments had the potential to describe the impacts of drought on the vegetation growth. The strong El Nino during 2015 and 2016 peaked in late 2015, and ended in May 2016 [36]. We further examined the seasonal cycles of the vegetation and climate variables (precipitation, EVI, SIF, and \(\text{SIF}_{\text{yield}}\)) over drought period (from May 2015 to April 2016) in the two sub-areas (Figure 6). For the cropland, the values of EVI were higher than the multiyear mean for most months during the drought period (Figure 6c). SIF had larger reductions than EVI during the early stages of the drought (May to August 2015). As a consequence, SIF had more potential than EVI to monitor the spatial patterns when drought occurred in cropland areas. The \(\text{SIF}_{\text{yield}}\) was much smaller than the multiyear mean for most of the dry months (Figure 6g) and had a similar trend in precipitation (Figure 6a). During the drought period, SIF was affected by higher radiation which can offset the negative impacts of water stress, leading to the smaller decrease of SIF compared with the large negative anomalies of \(\text{SIF}_{\text{yield}}\). Figure 5: The probability density of normalized anomalies of **(a,b)** Precipitation, (**c,d**) EVI, (**e,f**) SIF, (**g,h**) \(\text{SIF}_{\text{yield}}\) in 2015 and 2016 relative to that of over the period 2007–2014 in croplands (left column) and evergreen broadleaf forest area (right column).