## 3 Results
### Relationship between SIF and GPP
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Integration Time** & **30 min** & **30 min** & **30 min** & **6.5 h** & **6.5 h** \\ \hline
**Sky Conditions** & **All** & **Clear Sky** & **Clear Sky during the Whole Day** & **All** & **Clear Sky during the Whole Day** \\ \hline data points & 768 & 313 & 173 & 72 & 17 \\ \hline SIF687 & & & & & \\ \(r^{2}\) & 0.25 & 0.16 & 0.33 & 0.34 & 0.58 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \(t\)-statistics & 16.05 & 7.73 & 9.21 & 6.07 & 4.57 \\ \hline SIF760 & & & & & \\ \(r^{2}\) & 0.58 & 0.52 & 0.61 & 0.83 & 0.88 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \(t\)-statistics & 32.46 & 18.31 & 16.39 & 18.28 & 10.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of the linear regression between SIF and GPP as a function of experimental conditions. Integration time: 30 min, SIF and GPP are averaged over 30 min; 6.5 h, SIF and GPP are averaged between 8:00 and 15:30 UTC. Sky conditions: All, all measured points are included in the analysis; Clear sky, clear sky at the measuring time only; Clear sky during the whole day, only data from complete clear sky days are considered. \(p\)-value and \(t\)-statistics for the probability of the regression slope to be zero are indicated. Figure 8: Coefficient of determination from the linear regression between SIF760 and GPP as a function of measurement time in clear sky conditions. Red points: GPP is averaged over a 30 min interval at the same time as SIF760. Blue squares: GPP is averaged over the day from 8:00 to 15:30 UTC. Figure 7: **(a-d)** Scatter plots of GPP and SIF. **(a,b)** Half-hourly averages; **(c,d)** Daily averages over the period 8:00–15:30 UTC; **(a,c)** GPP is plotted against red SIF in the O\({}_{2}\) B band (SIF687); **(b,d)** GPP is plotted against far red SIF in the O\({}_{2}\)-A band (SIF760). Red (gray) points indicate clear (overcast) sky conditions. Coefficients of determination of the linear regression between GPP and SIF and \(\pm 1\sigma\) confidence bands are reported. All data points are considered in the regressions presented here. Regression lines equations are : **(a)**\(38.48x+8.73\); **(b)**\(16.75x+8.95\); **(c)**\(44.4x+5.7\); **(d)**\(17.62x+8.29\). To evaluate the effect of the crop state on the relationship between GPP and SIF, we performed linear regressions on data from restricted time windows (Figure 9). We found noticeable changes in the coefficient of determination (\(r^{2}\)) when the regression period (20 to 70 days) was time shifted from the beginning of crop development to senescence. Figure 9 shows the result of linear regression between GPP and PAR. It can be seen that when a crop was under development, \(r^{2}\) (GPP, SIF760) was much higher than \(r^{2}\) (GPP, SIF687). As a crop entered into a steady state, the relationship between SIF760 and GPP slightly weakened, and the relationship between SIF687 and GPP became stronger. When a steady state was reached during the time window, \(r^{2}\) (SIF760, GPP), and \(r^{2}\) (SIF687, GPP) were close to each other, and close to \(r^{2}\) (PAR, GPP). When the crop entered into the senescent phase, the difference between \(r^{2}\) (SIF760, GPP) and \(r^{2}\) (SIF687, GPP) increased again, and both \(r^{2}\) (SIF687, GPP) and \(r^{2}\) (PAR, GPP) decreased by almost the same amount. These results indicate that SIF687 does not <<explain>> GPP variance better than PAR does. Similar interpretations can be drawn when considering half-hourly data; however, with lower coefficients of determination (results not shown).