## 5 Conclusions  
We conclude, from the results of this demonstration study, that sun-induced fluorescence provides complementary information compared to commonly used RS based vegetation variables to characterize plant photosynthetic activity. F\({}_{760}\) can reliably be derived from medium and high resolution reflectance data and improves our capability to model dynamically occurring limitations in photosynthetic energy conversion. The PRI complements the F\({}_{760}\); however, its use is still problematic because of its cross-sensitivity to structural effects [9], pigment pool sizes [10], reflectance anisotropy [11], and illumination effects [11; 54]. This study demonstrates the necessity to include the physiological processes underlying the photosynthesis that can be determined with remote sensing approaches as part of calibration and validation campaigns to be conducted in the support of the FLEX satellite mission in the upcoming years using an already existing network such as OPTIMISE ([[http://optimise.dcs.aber.ac.uk/](http://optimise.dcs.aber.ac.uk/)]([http://optimise.dcs.aber.ac.uk/](http://optimise.dcs.aber.ac.uk/))). In the near future, we expect that retrieval algorithms will become operational that allow quantifying the two fluorescence peak features originating from the two functionally separated photosystems. This will largely boost our ability to mechanistically understand and interpret of limitations in the photosynthetic machinery. Further, the first high resolution imaging spectrometer for fluorescence retrieval (_HyPlant_) recently became operational and further insights into the spatio-temporal variability of the sun-induced fluorescence signal can be expected [42; 44].  
The following are available online at www.mdpi.com/2072-4292/8/7/574/s1, Table S1: Parameters characterizing the relation between NDVI and \(f_{\text{APAP}}\); Table S2: Parameters characterizing the relation between F\({}_{760\text{-}yield}\) and LUE\({}_{\text{EC}}\); Table S3: Parameters characterizing the relation between F\({}_{760}\)-yield PRI and LUE\({}_{\text{EC}}\); Figure S1: Example of the single exponential fit to derive the maximum net photosynthetic CO\({}_{2}\) uptake rate (A\({}_{\text{max}}\)) from a light responds curve; Figure S2: Maximum net photosynthetic CO\({}_{2}\) uptake rate (A\({}_{\text{max}}\)) of wheat and sugar beet.  
**Acknowledgments:** We gratefully acknowledge financial support provided by the SFB/TR 32 \"Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation\"--subproject D2 (www.tr32.de), funded by the Deutsche Forschungsgemeinschaft (DFG). The authors sincerely thank [PERSON] for the measurements and data processing of the Selhausen eddy covariance station. [PERSON] was supported by a grant of the Swiss University Conference and ETH-Board in frame of the Swiss Earth Observatory Network (SEON) project (www.seon.uzh.ch). [PERSON] was supported by the German Federal Ministry of Research and Education (BMBF) in the framework of the project IDAS-GHG (Grant 01 LN1313A).  
**Author Contributions:** [PERSON] conceived the study and designed the experiments. He was PI of the campaigns and organized data acquisition and analysis with the assistance of [PERSON], [PERSON], [PERSON] and [PERSON]. [PERSON] recorded the ground data and analyzed the data set. [PERSON] performed the statistical analysis and wrote with [PERSON] the manuscript. [PERSON] developed the fluorescence retrieval algorithm and analyzed the airborne data. [PERSON] performed the Merken eddy covariance measurements, their processing and the documentation of the eddy covariance measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.