## 1 Introduction  
Land Use and Land Cover (LULC) maps not only describe the composition and distribution of the natural elements on the land surface but also reflect the anthropogenic effects on these elements [1]. Remote sensing perfectly meets the requirements of LULC classification and monitoring due to its robust, consistent, repeatable and cost-effective capacities [2]. Sentinel-2 is a new generation optical satellite for land monitoring developed by European Space Agency for data continuity and enhancement of the Landsat and SPOT missions [3]. It has the combination of high spatial resolution and revisits frequently, novel spectral capabilities, and wide coverage, which offer more advantages over Landsat series in regional LULC classification. However, the existing researches mainly focus on parameter estimation (e.g., vegetation biophysical and water quality [4; 5; 6]) and specific target detection (e.g., water body, greenhouses, and built-up areas [7; 8; 9; 10]). Vegetation species classification has also attracted great attention with the aim of assessing the potential of its three unique red-edge bands [11; 12]. As with MODIS and ASTER, Sentinel-2 has the characteristics of inconsistent spatial resolutions. Then, unifying the spatial resolution of different bands is a prerequisite for many applications. Usually, there are two categories of schemes to execute the geometric unification. One is to upscale the fine resolution bands to match the coarse resolution bands. The other is to downscale the coarse resolution bands to match the fine resolution bands [13]. Choosing which scheme depends on the application purpose. For example, upscaling perfectly meets the requirement of lithologic mapping for ASTER with the ability of complete preservation of the original spatial and spectral information of its short wave infrared (SWIR) bands, which are the characteristic bands of minerals (i.e., clay, carbonates and sulfates) and reduce data volume at the same time [14; 15]. However, for LULC classification, downscaling is more suitable due to the capability of fully utilizing the valuable detailed information obtained by fine resolution bands [16]. In the context of remote sensing image processing, upscaling and downscaling are usually implemented by spatial interpolation. Nearest neighbor interpolation is the most popular one because of its straightforward implementation [17; 18]. Besides, pansharpening is also commonly used in downscaling [19; 20]. Pansharpening can consequently be defined as a pixel-level fusion, which is to merge the geometric details of a high resolution (HR) image into low resolution multispectral (LRMS) bands [21; 22]. It increases the spatial information at the cost of a certain degree of spectral information loss, represented by a more or less color distortion [23]. Typical pansharpening techniques include component substitution (CS), statistics, and multiresolution analysis (MRA) based methods. The fusion quality of different algorithms depends on data used and the application purpose [24; 25]. For example, the Intensity-Hue-Saturation (IHS) and Principal Components Analysis (PCA) transformation, which are based on CS, can achieve a satisfactory result for visual interpretation on Landsat TM, SPOT, and IRS. However, they are less effective on those data acquired from satellites launched after 1999 because of the PAN band wavelength extended from visible into the near infrared (e.g., IKONOS and QuickBird), or for quantitative analysis [23; 26]. As the sensor and application dependence of fusion quality, the effects of different image sharpening algorithms on LULC classification have been investigated using MODIS and ASTER [27; 28]. Although there are already several attempts in downscaling the Sentinel-2 data, they tend to focus on the quantitative analysis of fusion quality or its improvement in water extraction and vegetation monitoring, and the effects on LULC classification accuracy of Sentinel-2 are still untested and urgently needed [6; 7; 29; 30; 31].