## 4 Remaining Challenges and Open Research Questions  
Beyond the valuable advances achieved so far, several challenges are yet to be overcome and several issues related to the UAS-based methods for SIF retrieval remain open. First, the sensitivity of the cosine corrector to varying sun zenith angles, and the etaloning effect caused by the interfering of the light reflected in the borders of the back-illuminated thinned charge coupled device add uncertainty to SIF estimations. In addition, sensor calibration methods, data collection, and data processing protocols need to be standardized and spectroradiometers need to be characterized [46] in order to enable measurements from different instruments at diverse geographical locations and temporal periods to be directly compared. The main features to be characterized are the FWHM as proposed by [42], and the SNR, spectral sampling interval, and spectral shift, which were identified by [59] as the most impacting characteristics on SIF retrieved with FLD methods. Besides, inherent issues of the SIF retrieval must be considered, such as the higher variation and lower SNR generally obtained in the O\({}_{2}\)-B, and the lack of required information to run an accurate correction to diminish the effect of atmospheric interference. Besides the sensor-associated issues, complementary studies should be conducted suggesting ways to improve the FOV geolocation accuracy, and the spatio-temporal match between on-board and proximal measurements of same targets. Indeed, robust validations of UAS-based SIF estimations are up to now hindered by these spatio-temporal discrepancies of the two levels of information, since their comparison is not fully convincing with differences of few meters and/or minutes. Some ideas have been proposed to improve the spatio-temporal match between UAS-based and ground SIF retrievals aiming at conclusive validations; for instance, in [40] the authors suggest to overlay the spectrometer data with RGB imagery derived maps, while the authors of [51] recommend the use of real time kinematic (RTK) correction for the geolocation of footprints, plus the implementation of lever arm and boresight corrections [52]. Moreover, the precise characterization of the spectroradiometer footprint might also be achieved with the support of automated ground platforms for SIF assessment, particularly those developed and used in plant phenotyping. These instruments can be programmed to collect SIF data at specific points with an accuracy of few seconds and centimeters, which consequently can be precisely aligned with the moment and place of the UAS-based data points. Some ground platforms for SIF phenotyping can be configured to collect information at specific sensor angles and AGL elevation, therefore they might also be used for optimal UAS flight simulations with a precise pose characterization and footprint spatio-temporal location, targeting to (i) elucidate the real maximum scope of UAS-based methods for SIF retrieval, which is so far unclear, (ii) facilitate the robust validation of aerial retrievals, thus (iii) allowing deeper analyses and understanding of the SIF estimated from low altitude hyperspectral data, e.g., throughout the comparison of retrieval methods, and a thorough comprehension of the sensor multi-angularity effect. Furthermore, according to [60], the effect of the atmospheric conditions should be incorporated into the retrieval throughout model-based approaches notwithstanding if the data is acquired at proximal, airborne, or orbital level, which opens another query: would the accuracy of UAS-based SIF retrievals be significantly increased by implementing new dedicated atmospheric correction methods where current models are not easily adapted? In addition, the authors of [61] reported the impact of the bidirectional reflectance distribution function (BRDF) on the canopy chlorophyll content estimation from UAS hyperspectral imagery.