## 7 General Conclusions  
In this paper, we describe the satellite-based retrieval of a sun-induced far-red vegetation fluorescence derived from GOME-2A spectra. We followed the general retrieval approach described in [PERSON] et al. (2011) and implemented our own retrieval setup that differs in a number of important \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference Area** & **Month** & **RMS** & **Mean** & **STD** & **R** & **Slope** & **Intercept** & \(N\) \\ \hline \multirow{4}{*}{Cloudy ocean} & January & 0.33 & \(-\)0.19 & 0.27 & 0.91 & 1.07 & 0.15 & 28,844 \\  & April & 0.37 & 0.24 & 0.28 & 0.95 & 0.61 & \(-\)0.01 & 37,706 \\  & July & 0.58 & 0.41 & 0.41 & 0.93 & 0.58 & 0.03 & 44,328 \\  & October & 0.27 & 0.11 & 0.25 & 0.89 & 0.82 & \(-\)0.01 & 40,786 \\  & **All** & **0.42** & **0.17** & **0.38** & **0.86** & **0.65** & **0.07** & **151,664** \\ \hline \multirow{4}{*}{Snow/ice} & January & 1.06 & \(-\)0.68 & 0.82 & 0.06 & 0.08 & 0.65 & 28,844 \\  & April & 0.79 & \(-\)0.42 & 0.67 & 0.09 & 0.06 & 0.35 & 37,706 \\ \cline{1-1}  & July & 0.84 & \(-\)0.46 & 0.71 & 0.55 & 0.36 & 0.58 & 44,328 \\ \cline{1-1}  & October & 0.55 & \(-\)0.21 & 0.51 & 0.52 & 0.49 & 0.32 & 40,786 \\ \cline{1-1}  & **All** & **0.81** & **\(-\)0.42** & **0.69** & **0.35** & **0.29** & **0.49** & **151,664** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Similar to Table 3 but for tests with different reference spectra. Reference spectra for the PC analysis from cloudy ocean or snow/ice-covered areas are compared to desert areas (baseline). All correlations are highly significant. aspects from version 26 of the NASA data set. In particular, we have used desert only as reference areas for determining the PCs to describe atmospheric absorption optical thickness. Furthermore, reference spectra were collected from a year of data. We used a much wider fit window (712-783 nm) that also covers water vapour absorption up to about 740 nm and absorption in the oxygen A band. Finally, we needed a larger number of PCs because atmospheric transmission shows more spectral variation across our fit window. The two time series correlate well with an overall R of 0.78. However, we also observe that over tropical rain forests correlations drop to background levels. Subsequent sensitivity experiments investigating the effect of the number of PCs, the fit window, and the reference area for PC determination, suggest the strong effect on the retrieval outcome of whether or not the water vapour absorption band is included in the fit window. Future research may indicate whether this or insufficient natural variability (compared to noise levels) explains the drop in correlations for tropical rain forests. Furthermore, we have compared the SiF time series with Gross Primary Productivity derived from flux tower measurements in Australia. The correlation between SiF and site GPP ranged from 0.37 to 0.84. Managed biome types typically have higher correlations than natural types. Finally, by using the de-seasonalized SiF time series for Australia, the break of the Millennium Drought during the local summer 2010/2011 could be detected. Vegetation activity increased from January 2007 to April 2011 due to the transition from droughts to increased rainfall in the summer 2010/2011. Decreases in SiF were seen after April 2011 and may be due to less rainfall and higher temperatures towards 2015. Analysing SiF time series illustrates the strong potential of SiF data to monitor vegetation activity in relation with meteorological anomalies. This work has been co-funded by the Netherlands Organization for Scientific Research, NWO Vidi grant 864.09.001. This work utilized flux tower data collected by grants funded by the Australian Research Council (DP0772981, LP0990038, DP130101566, DP0451247, DP0344744, LP100100073, LE0882936). [PERSON] is funded under an ARC Future Fellowship (FT110100602).