## 3 Results  
We found a strong, overall correlation between the \(T_{\rm air}\) and LST estimates (\(r=0.81\)), but there was marked variation among the different vegetation classes (Figure 1). There was a general tendency for air temperature to underestimate remotely sensed LST and this was especially pronounced for non-forest sites.  
Overall, the P-model simulations of GPP showed good agreement with the eddy-covariance values (Figure 2). There was, however, a general finding (indicated by the bias and slope metrics in Figure 2) that the simulations tended to underestimate the GPP data. Simulations forced with remotely sensed LST did not consistently perform better than the \(T_{\rm air}\) predictions.  
Figure 1: Scatterplots comparing ECMWF gridded air temperatures with Sentinel3 daytime LST estimates; organised by vegetation class: evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrublands (CSH), open shrublands (OSH), savannahs (SAV), grasslands (GRA), permanent wetlands (WET) and croplands (CRO). The dashed red line shows the ideal fit. Each point is a ten-day average.  
A more nuanced pattern emerged, however, when relative performance was compared across vegetation categories (Figure 3). For ENF sites, the largest category in the dataset (Table 1), the two simulations were virtually indistinguishable even under drought conditions. However, for certain classes (e.g., DBF) the LST simulations performed better than \(T_{\text{air}}\); and that advantage was enhanced in dry periods. Model performance was somewhat weaker (higher RMSE) during periods of drought.  
Figure 3: Error estimation (GPP simulations versus eddy-covariance) by vegetation class with the number of participating sites indicated. Those periods identified as unusually dry (SPEI \(<-1.5\)) are shown in red and contrasted with the full timeseries (all, in blue). Bars relate to simulations driven by S3_LST and dots to ECMWF gridded T\({}_{\text{air}}\).  
Figure 2: Goodness of fit: GPP simulations versus eddy-covariance estimates. Left panel—forcing temperature is Sentinel3 LST; Right panel—forcing temperature is ECMWF gridded air temperature. The intensity of the colours (heat-map) is designed to indicate the density of points. Each point is a ten-day average. The dashed-grey line shows the ideal fit.  
Time-series plots allowed us to investigate seasonal variation in model performance for individual sites; Figure 4 provides examples for contrasting vegetation classes including: Norunda in Sweden, a region where the effects of the 2018 drought were especially marked [27]; and Hesse in France, the primary site analysed in the [PERSON] et al. [31] study of the 2003 European drought. The P-model simulations reproduced the eddy-covariance patterns realistically, but at certain sites (e.g., Hesse in France, Figure 4) the predictions over-estimated productivity in spring. The pronounced drought of the summer of 2018 was not evident at all sites (e.g., Font-Blanche in southern France, Figure 4) and there was little evidence that productivity was markedly lower in 2018 compared to 2019. In aggregate, our analysis suggests that simulations forced with LST performed better than \(T_{\text{air}}\) equivalents for certain sites and vegetation categories--especially sparsely vegetated categories (e.g., shrublands and savannas, Figure 3). However, the predictions, even forced with LST, do not fully capture the observed effects of drought on productivity--notice the gap between eddy covariance estimates and simulations at Font-Blanche during summer 2019 (Figure 4).  
Figure 4: Seasonal variation in GPP estimates for selected sites. Inferred (eddy-covariance) values are shown as points: those periods with a site-specific SPEI \(<-1.5\) are indicated in red. GPP simulations are shown as lines: forced with ECMWF gridded air temperature (purple) and forced with Sentinel3 daytime LST (cyan). Notice that the ranges on the y-axes vary between the plots.