## 4 Discussion  
Overall, the results of the validation of SIF/PAR against in situ eddy-covariance estimates reveal the potential of SIF/PAR as a diagnostic of transpiration efficiency. This transpiration efficiency is understood here as the ability of the land cover to meet the atmospheric demand for water, thus may relate to purely phenological variability (e.g., LAI dynamics) as well as to physiological stress related to e.g., extreme climate. SIF/PAR exhibits higher correlations against the in situ data compared to any of the earth2 Observe models, despite its coarser resolution (0.5\({}^{\circ}\) versus 0.25\({}^{\circ}\)). In a few sites, some models exhibit equivalent or higher performance than SIF/PAR. It is important to note that despite smoothing daily values and using biweekly averages, sensor noise may still be prevalent in SIF/PAR calculations. Nonetheless, the [PERSON] et al. [47] GOME-2 retrievals tend to exhibit less noise than those from other methods (see ibid.). Future satellites like the European Space Agency's Fluorescence  
Figure 4: SIF/PAR (**a**) and model ensemble \(\tau\) (**b**) are averaged across each season (December, January February (DJF); March, April, May (MAM); June, July, August (JJA); and September, October November (SON)) between 2007–2014. Higher values of both SIF/PAR and \(\tau\) (green regions) indicate unstressed conditions and dense vegetation, while lower values (red regions) indicate greater transpiration constraints. Pixels designated as barren or snow and ice based on the International Geosphere-Biosphere Programme (IGBP) classification are masked out. Pixels with less than 30 days of observations per season are also excluded. Explorer (FLEX) or the recently-launched Sentinel 5P (mounting the TROPOMI sensor) are expected to improve these insufficiencies. When analyzing the validation results grouped by IGBP biome class, SIF/PAR is particularly correlated with the in situ data over densely forested sites, along with grasslands and croplands. SIF/PAR exhibits marked improvements in correlations (0.67) compared to the model average (0.47) for these particular IGBP classes. In contrast, evergreen broadleaf and needleleaf forest sites, like US-Me2, show lower correlations; [PERSON] et al. (2018) found similar results when relating SIF with tower GPP (see Figure S3). Further validations indicate that SIF/PAR captures the timing of transpiration efficiency accurately (Figure 2c), and may thus be used to evaluate drought periods. While other optical vegetation indices can be considered, SIF is the only physically-representative remote sensing variable that is expected to capture short-term dynamics in vegetation activity, thus reflect the early stages of transpiration stress. Consequently, SIF has been shown to exhibit the shortest lags with respect to the in situ estimates of latent heat, when compared to traditional optical indices such as LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) (see [PERSON] et al. (2018); [PERSON] et al. (2018)). A potential limitation of our analysis is the lack of validation towers in the Southern Hemisphere, and the fact that not every IGBP class is represented by the 25 towers used for validation. Therefore, the overwhelming majority of towers used in this analysis are clustered in northern mid to high latitudes. Moreover, these sites experience seasonal snow in winter, corresponding to times of declining GPP. Subsequent precipitation and snow masking limits the number of observations, which may explain why the correlations against the in situ observations are slightly lower during the vegetation decay period (Figure 2c). Furthermore, SIF is not corrected for angular anisotropy, which can negatively impact the quality of the observations (Stein  
stress and leaf area changes, by demonstrating sensitivity to other factors such as sub-optimal radiation and temperature levels.