## 4 Conclusions  
The automation of the HyPlant processing chain in recent years now enables the processing of numerous flight lines in a short time period. This is very important, since HyPlant is the airborne demonstrator for the future FLEX satellite mission and has thus been used in many airborne surveys to exploit the potential of spatial SIF information for terrestrial and aquatic ecosystems. The fast data processing will also benefit future applications which require the data and derived products, for example, in decision-making processes in agriculture and breeding, where SIF maps, in particular, can be used as an indicator of the current status of plants. Although SIF is becoming a commodity, a large range of instrumental, atmospheric and analytical factors complicate its retrieval, and may ultimately compromise the interpretation of retrieved SIF values. The development of the consistent and complete HyPlant processing chain has also unraveled hidden challenges that can impede a reliable and efficient SIF retrieval (i.e., PSF non-uniformities), and it has enabled the cross-comparison and evaluation of several SIF retrieval schemes. This is particularly important to identify and define cases where such methods perform best or should not be applied. The close relation of SIF to the important ecosystem process of photosynthesis and related gas exchange explains the great interest in SIF among the scientific community. However, SIF is one of various information sources needed to constrain estimates of these complicated ecosystem processes. Several other information can be revealed from HyPlant data, but require a consistent processing in terms of sensor calibration, and atmospheric and geometric correction. Further harmonized data processing and SIF retrieval is essential--capable of accounting for sensor re-calibration and degradation, and able to compensate for changing environmental conditions in order to exploit the temporal evolution of SIF and compare information acquired across ecosystems. The HyPlant processing chain is one example of a processing infrastructure and will be further streamlined to enable cross-time and space data acquisition to further advance SIF research. B.S. with input of U.R., A.D., L.G., S.C., N.S., L.A., M.C., S.D., F.Z., M.P., P.R., T.K. and J.Q.V., performed substantial parts of the data processing and wrote the paper. U.R. was the PI of the 2018 ESA FLEXSense campaign and organized the data acquisition in Germany with the assistance of B.S., P.R., M.M. and D.S., T.K. helped with the coordination of the HyPlant data acquisition at the agricultural research station Campus Klein-Altendorf. L.A., M.C., S.C., R.C., A.D., S.D., L.G., J.H., K.K., M.M., J.M., O.M., M.P., F.P., P.R., F.R.-M., N.S., A.S., D.S. and F.Z. were involved in the development and improvement of the HyPlant processing chain at various stages in recent years. [PERSON] and [PERSON] conceived the study and designed the paper with discussions and contributions from all co-authors in the process of writing. We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition, development of the HyPlant processing chain and data analysis in the frame of the HyFLEX campaign (ESA contract no. 4000107143/12/NL/FF/If CCN3), the FLEXSense campaign (ESA Contract No. RFP/3-15477/18/NL/NA) and the HYPER project (ESA Contract No. 4000112890/14/NL/FF/gp). The Deutsche Forschungsgemeinschaft (DFG) provided additional financial support by the SFB/TR 32 'Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation' project D2.