## 1 Introduction
Enabling identifying and quantifying the role of leaf-canopy-atmosphere variables in determining TOA radiance requires a rigorous sensitivity analysis that takes all interactions into account. Such systematic analysis can be achieved by means of a global sensitivity analysis (GSA) [28]. GSA provides information on how the variation of model output is produced by the variation of model input variables individually and globally through interactions with each other [29]. Hence, GSA enables to identify the influential and non-influential input variables for a model output, e.g., TOA radiance along the 400-2500 nm spectral range. The drawback of GSA methods is thatthey are computationally expensive and complex because of the required large number of model evaluations [28]. This is an important issue when coupled vegetation-atmosphere RTMs are used, since the computational burden of such coupled models can be substantial [19; 30]. To overcome this computational burden it has been proposed to make use of emulation [31; 32; 33]. Emulators are statistical models that approximate the input-output results of an RTM by means of machine learning [32], and this at a fraction of the RTM computational cost. This technique has been earlier proven successful in GSA studies of advanced physical models in various domains to enable identifying driving variables [19; 32; 34; 35; 36; 37], and will be further explored in this work. Having entered the era of the Sentinels, the opportunity arises to develop retrieval algorithms directly from S2 L1C data, i.e., at-sensor TOA radiance data. Accordingly, the pursued approach is as follows: first an emulator from a surface-atmosphere model is developed as an approximation of the original RTMs in order to identify the variables through a GSA of TOA radiance in the entire visible and near infrared (VNIR) to shortwave infrared (SWIR) spectral range at a spectral resolution of 1 nm. Based on these GSA results, biophysical variables retrieval strategies applicable directly to an at-sensor TOA radiance dataset will be developed. From past experiences where different retrieval methods have been compared for S2 data at TOC scale [38], the so-called hybrid retrieval methods, i.e., where RTM data is used for training machine learning methods, are particularly promising in terms of accuracy and processing speed [2; 39]. Here, the developed retrieval algorithms should eventually be applicable to S2 L1C products, thereby avoiding the uncertainties of the atmospheric correction process [40]. Altogether, this study boils down to the following objectives: (1) to develop emulators to approximate the coupled PROSAIL-MODTRAN RTMs for a set of input variables and TOA radiance output; (2) to apply the emulator into a GSA in order to identify the driving variables; and finally, as a proof of concept, (3) develop hybrid retrieval models for biophysical variables from S2 L1C (TOA radiance) and L2A (bottom-of-atmosphere reflectance) data. All these objectives have been tackled with an in-house developed software framework that is made freely available to the community. The remainder of this paper is structured as follows. Section 2 gives a further insight into S2 mission with specifications about instrument characteristics and its atmospheric correction (Sen2 Cor) and biophysical retrieval algorithms. Section 3 presents the software framework, RTM configurations and toolboxes used to conduct the emulation, GSA and the TOA retrieval performance assessment strategy. This is followed by presenting the results in Section 4 which are discussed in a broader context (Section 5). Section 6 concludes this paper.