## 1 Introduction
Finally, products are available with daily temporal coverage that blend different types of data, such as the Canadian Meteorological Centre (CMC) snow product [47], a combination of weather stations and climate models with a spatial resolution of 24 km; the ESA's GlobSnow product [48], with 25 km resolution, which integrates satellite data, station observations, and models; and the Interactive Multisensor Snow and Ice Mapping System (IMS) [49] produced by the U.S. National Ice Center, with 1 km resolution. In summary, no sensor is currently available that provides both adequate spatial and temporal resolution for fine-scale spatio-temporal studies. Several methods have been proposed to overcome this limitation and provide time series of snow cover without gaps, which can be divided into methods based on gap-filling of binary snow data, i.e., snow/no snow maps, gap-filling of continuous snow data, such as fractional snow cover (FSC) or NDSI maps, and, lastly, methods based on the fusion of different data sources. Among the gap-filling of binary data methods, SNOWL is a spatial filtering method based on snow transition elevation developed for MODIS [50]; simple and easy to implement, but not suitable for fine-scale analysis. The locally weighted logistic regression (LWLR) computes the snow cover probability of the cloudy pixel based on the topographic and spatial relations with its neighboring cloud-free pixels [51]. Examples of temporal filtering are the Terra and Aqua combination (TAC), and the three-days-temporal-filtering (3 DTF), which, respectively, use the two daily MODIS images and the images acquired on the previous and following day to fill in the gaps due to clouds [52]. A multi-day-combination approach was used by [53] to replace cloudy pixels with cloud-free pixels in a temporal interval of up to 1 month. These methods benefit from a simple application, but, as the temporal window increases due to persistent cloud cover, the temporal resolution and accuracy of the result will decrease. Other authors have proposed techniques based on the gap-filling of continuous data. The spatial and temporal adaptive gap-filling method (STAGFM) was proposed by [PERSON] et al. [54] for gap-filling of MODIS NDSI data based on the concept of the similar pixel, i.e., the pixels with smaller NDSI differences from the central pixel, which, when covered by clouds, can be predicted by its weighted similar pixels. This was developed into the conditional probability interpolation method based on a space-time cube (STCPI), which combines a space-time cube and conditional probability interpolation method to produce daily gap-free products [55]. The accuracy of both methods depends on the availability of cloud-free pixels. Other authors have proposed the non-local spatio-temporal filtering (NSTF) method to retrieve cloudy pixels by using the appropriate similar pixels in the remaining known part of an image via an automatic machine learning technique and the use of ancillary data, such as land cover [56]. More recently, several methods based on data fusion have been adopted, fusing SAR and optical data or coarse and high resolution optical data, such as MODIS and Landsat or Sentinel-2 imagery. [PERSON] et al. [57], through an object-based principal component analysis-support vector machine (PCA-SVM) method for snow cover mapping, attempted to merge MODIS snow cover products and the Sentinel-1 scattering; however, the variations of the polarization parameters were not sufficient to differentiate snow from non-snow cover, thus leading to misclassification and false detection in the snow pixel classification. [PERSON] et al. [58] proposed a machine-learning-based method to fuse Landsat and MODIS images and several ancillary data, some of which were hardly available, to obtain daily images at 30 m resolution, requiring very high computational resources. Another recent work is that of [PERSON] et al.