## 4 Discussion
### OCO-2 SIF for Studying Drought Impact  
OCO-2 with its increased data acquisition frequency and high spatial resolution was shown to be a promising tool to study large-scale drought impact on vegetation. Although the local pass time of 1:30 PM of OCO-2, allows for a more accurate estimate of SIF\({}_{\text{yield}}\) sensitivity to physiological plant water stress [46], there is a possibility of overestimation of this sensitivity during drought conditions. A combination of derived, improved SIF retrievals from GOME-2 (e.g., SIFTER, [33]), which has a local pass time of 9:30 AM, and OCO-2 should be used to explore and understand the morning-afternoon SIF variation during drought conditions. To better understand the mechanism of stress-induced fluorescence yield variation as captured by the OCO-2 SIF, models that explicitly represent SIF processes (e.g., SCOPE model) should be applied [31; 93; 94].  
The OCO-2 satellite provides a finer spatial resolution SIF data at the cost of temporal resolution (revisit time of 16 days). This might lead to errors due to sampling effects for drought impact studies, especially when comparing with MODIS composite products, as the OCO-2 SIF represents an instantaneous measurement (at 1 PM) performed every 16 days, whereas the MODIS composite represents the best value over a period (mostly 16 or 8 days). This sampling effect might also explain the two peaks in SIF data in broadleaved forest (Figure S7), and this effect is not observed in the NDVI data (Figure S4). However, in our study we deem this effect to be negligible as phenological changes in broadleaf forest during the spring warming of 2018 was well captured despite (Figure 4 and Figure S7) a low and dispersed representation of only 4% of the total study area (Table S3). Moreover, the SIF footprint location keeps changing during every revisit, as the satellite does not operate in a typical \"pushbroom\" fashion as other sun-synchronous satellites like Sentinel, Landsat, MODIS, etc. This does not allow for a pixel-based temporal analysis of the SIF data. However, with a higher data acquisition rate, this provides a great potential to study large-scale events [42]. Furthermore, a large-scale spatial aggregation of OCO-2 SIF also reduces the uncertainty in OCO-2 SIF measurement owning to higher clear-sky data acquisition rate [42].  
Our study also demonstrates the strong added value of SIF-products in comparison to more conventionally used products (NDVI, fPAR), when considering short reference periods (Figure 6 and Figure S4). Moreover, the new sensor of the OCO family (OCO-3) was recently launched on 3 May 2019, which has the same physical and measurement characteristics as compared to OCO-2. With this, a longer time series of high temporal frequency and spatial resolution SIF will be available in the near future. As presented in this study, OCO-2 SIF is sensitive to drought and can serve as a promising and potentially better satellite-based alternative to study impact of extreme events (droughs/floods/heatwaves) on terrestrial ecosystems. To further validate the potential of OCO's SIF for such studies, a combination of satellite derived vegetation indices, photochemical reflectance index (PRI), SIF (e.g., TROPOMI), and flux data could be considered. A more in-depth site-based study is also recommended to get more insight into ecophysiological stress mechanism in forests during drought. Furthermore, an interpolation of OCO's dataset through machine learning and statistical tools [34; 95] to cover the vacant areas may provide solutions for more rigorous analyses.