## 1 Introduction  
Retrieving spatially-explicit vegetation biophysical variables from space is one of the main goals of optical remote sensing, and one of the objectives of international space programs such as NASA Earth Observation Systems or the European Copernicus satellites constellation [1; 2]. Particularly, with the Sentinel-2 (S2) constellation an unprecedented inflow of optical data emerged for vegetation monitoring applications with an optimized balance between high spatial, spectral and temporal resolution [3]. The land surface reflectance for retrieval of biophysical variables is estimated from these satellite observations through atmospheric corrections [4; 5]. However, accurate atmospheric correction strategies need exact atmospheric variables from the satellite data itself e.g., [6; 7] or from external meteorological sources such as AERONET [8] or ECMWF [9]. As the retrieval is based on all kinds of assumptions regarding the model used and the retrieval method applied this step remains challenging, with potentially large uncertainties in the derived atmospheric characteristics and error propagation into surface reflectance [10]. To avoid the limitations of retrieving biophysical variables from surface reflectance data, some studies have demonstrated the possibility to determine biophysical variables directly from at-sensor top-of-atmosphere (TOA) radiance, [11; 12; 13; 14; 15] without the necessity to go through the atmospheric correction process [11; 12]. The downside of these approaches, however, is that they are not straightforward; they require a sound physical understanding on the factors determining the at-sensor spectral TOA radiance, e.g., as studied in [16; 17; 18]. It implies that biophysical variables retrieval from TOA radiance data have so far been restricted to experimental studies. With the purpose of democratizing these approaches to the broader community, what is lacking is a freely available, streamlined and generic processing framework that enables to automate retrieval applications directly from TOA radiance data. At-sensor spectral TOA radiance is the combination of radiometric effects from surface reflectance, atmospheric effects and target surroundings convolved with the sensor spectral and spatial response functions [19]. Consequently, the identification of the key input variables that drive TOA radiance is a first mandatory step to retrieve biophysical variables directly from at-sensor TOA radiance data. Once having the drivers along the spectral range identified, it opens the door to develop dedicated TOA radiance retrieval algorithms for optical sensors such as S2, taking into account the wavelength-dependent role of the atmospheric factors. These drivers can be theoretically identified by means of coupled surface-atmosphere radiative transfer models (RTMs). Optical RTMs provide a physical interpretation of light interactions within a medium, e.g., leaf, canopy and atmosphere, and are based on solving the radiative transfer equation. To exploit at-sensor TOA radiance from vegetated surfaces, we need to consider three scales: (1) leaf, (2) canopy and (3) atmosphere; which are associated to two groups of RTMs: vegetation and atmospheric RTMs. Vegetation RTMs study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The two most widely used models are the leaf model PROSPECT [20], and the canopy model SAIL [21]. The coupling of these two models, named PROSAIL, has been used for over 30 years in sensitivity and retrieval studies [22; 23]. Atmosphere RTMs study the interaction of radiation with the atmosphere, on its way to the surface, and reflected back to the sensor. MODTRAN is among the most widely used RTM for atmospheric simulation and correction due to its accurate simulation of the coupled absorption and scattering effects [24; 25]. Accordingly, the coupling of PROSAIL with MODTRAN allows assessing the leaf, canopy, and atmosphere variables [11] that drive the observed at-sensor TOA radiance [26; 27].