## 5 Discussion
### TOC and TOA Retrieval Models  
When it comes to the developed TOC and TOA retrieval models, the relevant bands as given by the GPR band sigmas (\(\sigma_{b}\)) (Figure 6) are supposed to be in agreement with the obtained GSA results (Figure 5). The bands with lowest \(\sigma_{b}\) are expected to fall within regions of high sensitivity towards the targeted variable. * Regarding the Cab models, the most relevant bands (low \(\sigma_{b}\)) for both TOC and TOA fall within the visible region which is justified by the high sensitivity of Cab. The \(S_{Ti}\) rapidly declines when entering the red edge, which is also observed by the higher sigmas. Of interest hereby are the relatively high importance of the two SWIR bands, even though the GSA results show Cab has no influence there. This has to be interpreted by indirect co-varying relationships between LAI and Cab. After all, Cab absorption only occurs when leaves are available (which in turn reduce the role of soil background). The amount of leaves is controlled by LAI [53; 87]. * Regarding the Cw models, the most relevant bands for both TOC and TOA are found in the 1610 and 2190 nm SWIR bands. These are regions where Cw plays an important role. Further, the \(\sigma_{b}\) band ranking suggest that also the visible bands are of importance, which can be again attributed to co-varying relationships with other leaf properties such as Cab and the amount of leaves, i.e., LAI [53; 87]. * Regarding the LAI models, relevant bands are found all throughout the spectra with lowest \(\sigma_{b}\) in the red (665 nm), and especially in the two SWIR bands. This is again in agreement with the GSA results where LAI is dominant in the SWIR. We subsequently applied the TOC and TOA models to S2 L1C and L2A subsets for mapping applications over the Barrax agricultural site. The obtained maps merely serve as proof of concept to demonstrate that retrievals can be directly obtained from L1C TOA radiance data, i.e., without the need for an atmospheric correction. While results are encouraging, it must be pointed out that the models are still premature to make them produce accurate estimates for each pixel. Validation against ground truth data and fine tuning of the models is still required, e.g., accounting for the diverse variability of non-vegetated surfaces present in a S2 image, yet that is considered as beyond the scope of this work. Here we merely present the streamlined processing framework for the development of vegetation properties retrieval models applicable to at-sensor TOA data made available to the community. In this respect, in view of applying the presented tools for mapping applications, there are some opportunities for improvements that deserve to be mentioned:  
* Obtained maps from L1C and L2A data are surprisingly consistent given that no optimization steps were applied. Yet, it must be remarked, the images were acquired on a clear-sky summer day for a flat surface, making that the role of atmosphere is predominantly homogeneous and predictable. Obviously the retrieval from TOA data will be more challenging in a more rugged terrain and in atmospheric heterogeneous conditions, e.g., haze, clouds and shadowing effects. With the offered toolboxes (ALG, TOC2 TOA, GSA, retrieval) these effects can be studied, and specific retrieval strategies developed. * The TOC and TOA models were trained by simulated data using RTMs that deal with spectral variability of homogeneous vegetated surfaces. Although 20 soil spectral signatures were added to the training, that is definitely not enough to cover the natural variability of non-vegetated surfaces at S2 spatial resolution for complete images. For instance, the models are not trained for water bodies and man-made surfaces. Ideally, spectral variability of all kinds of non-vegetated surfaces should be added to the training dataset. Similarly, most likely the model performs poorly over heterogeneous vegetated surfaces such as forests.