## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Quality Check
#### 4.3.1 Core Sensor Metadata and Target Description  
In general, spectral measurements are only reproducible, interpretable, or comparable when the sensor and scene metadata are captured and stored in the appropriate format. They contain information about the sensor, the optics, and the sampling setup, essentially describing the sensor location, sampling geometry, and instrumentation used. In combination with the measurement protocol and processing descriptors, these metadata should account for the base spectral variability in the signal acquired with the given illumination-target-sensor setup (Figure 1). Modern spectral information systems are capable of storing all potentially relevant meta parameters [132] and target descriptors (including photographs of the target), or they can be easily extended to do so [133]. The selection of the required metadata is thus essentially dictated by science rather than the capabilities of information systems. The metadata set for a given application comprises core, application-specific, and non-critical metadata [134]; the core would apply to any spectroradiometric data, while F-specific metadata fall into the second category. Application-specific metadata for spectral acquisitions intended for F retrieval are more demanding in several ways compared with acquisitions for the purpose of reflectance calculations. The metadata attributes proposed for spectroradiometric data with the further objective of F retrieval are based on the current metadata model of the SPECCHIO spectral information system [135], which enables the standardized storage of spectra ([[http://www.speechio.ch/](http://www.speechio.ch/)]([http://www.speechio.ch/](http://www.speechio.ch/))). An extension of these core spectral metadata categories--including the instrument, instrument settings, and auxiliary instrumentation (such as goniometers or light sources, optics, and sampling geometry)--can be found in the SPECCHIO User Guide [136] or within the SPECCHIO application, in which the required additions are indicated (please see Supplementary Material, Supplementary Table S1). They include a better representation of both the sensor and optics characteristics (e.g., FWHMs or SRFs, FOV Response Function), data links (irradiance data link, uncertainty information linked to the traceability chain), environmental conditions, and the target description (e.g., target surface or 3D model). Among them, sensor characterization and uncertainty quantification deserve special attention [101]. Uncertainty propagation modeling is common in the field of remote sensing [137] but less common in proximal sensing applications [138]. However, the strong irradiance dependency of the F signal makes precise acquisitions of both irradiance and canopy radiance challenging and increases the sensitivity to random and systematic errors. Expanding on the former, the authors of [139] and [38] carried out analyses that highlighted the need adequately understanding how field spectroradiometers function and the impact of different sources of instrument-induced errors on F retrieval. These works also demonstrated that it is necessary to propagate uncertainties to the retrieval of F in order to understand the extent to which certain sources of error can be characterized and corrected for. Further efforts are needed for the establishment of uncertainty characterization and propagation procedures in the proximal sensing of F and are strongly encouraged by the community.