## 5 Conclusions  
Snow is an important driver of both biotic and non-biotic processes in mountain areas, both in terms of water intake and for its role of thermal insulation for low vegetation. The availability of high-resolution daily snow maps is critical to monitoring its dynamics. In this context, this study presents a random-forest-based data fusion workflow to obtain high-resolution daily images using S2 and MODIS data, built on predictive variables: (1) orographic (elevation, aspect and slope), (2) topographic (latitude and longitude), and (3) temporal (day of the year, DOY, and year). The RF in the first step carries out the actual prediction, while the second RF performs a downscaling. The results show that the proposed workflow obtains an accurate gap-free time series of daily snow cover maps at 20 m. We compared three different approaches, differing in the type of data selected as input: in the case of MODIS, the FSC or, alternatively, the NDSI, and in the case of S2, the SCE or, again, the NDSI. The first approach achieves the best accuracy, slightly higher than the other two, and outperforms the latter in terms of computational performances. The workflow requires only three types of data as input: MODIS and S2 snow data and a DEM at a resolution consistent with the latter, from which topographic predictor variables are retrieved. The most important predictor variables are the day of the year, elevation, and year in the first RF and MODIS, elevation and the day of the year in the final RF. The necessary input data are readily and globally available, making the workflow potentially straightforwardly reproducible in other study areas obtaining snow data critical for various types of fine-scale studies. The generalizability of the workflow, with respect to the study site and time period analyzed, will be investigated in the future. The tuning results show that this step is superfluous, and it is possible to directly use the default values to fit the RFs. The presence of snow in forest areas was not investigated in this study and remains a challenging issue, partly due to the lack of ground truth data in these areas. While the proposed method overcomes the limitations of detecting snow in cloudy conditions and the trade-off between high spatial and temporal resolution, it does not allow the study of time series prior to the start of the MODIS mission. A future improvement could investigate the integration of climate models, AVHRR data, or a combination of both, such as the dataset recently developed by [PERSON] and [PERSON] (2012), to overcome this limitation. Other future developments include the integration of a snow redistribution model by wind transport and avalanches ([PERSON] and [PERSON], 2013; [PERSON], 2014), and of Landsat images into the workflow to thicken the S2 collection and, most importantly, to extend the time series from 2000 to the present day ([PERSON], 2014). This time series, of about 20 years, will allow the evolution of fine-scale accumulation and melting of snow to be explored, crucial to understanding the ecological dynamics of the study area. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15020343/s1](https://www.mdpi.com/article/10.3390/rs15020343/s1)]([https://www.mdpi.com/article/10.3390/rs15020343/s1](https://www.mdpi.com/article/10.3390/rs15020343/s1)), Figure S1: Aspect map of the study area; Figure S2: Slope map of the study area; Figure S3: Producer Accuracy (PA) and User Accuracy (UA) for the classes Snow and No-Snow validated against independent dataset of Sentinel-2; Figure S4: Producer Accuracy (PA) and User Accuracy (UA) for the classes Snow and No-Snow validated against _in situ_ snow depth data; Figure S5: Tuning of the hyperparameters of the regression random forest fitted in step 1 of Approaches 1 and 2.