## 1 Introduction
Pansharpening techniques can be adapted to image sharpening of multispectral data (e.g., Sentinel-2) that have multiple spatial resolutions depending on bands and also multiple bands at the highest spatial resolution. The most straightforward way of adapting pansharpening techniques to image sharpening of multiresolution multispectral data is to select one of the highest resolution bands for each LRMS band as a PAN-like band [7; 13]. For Sentinel-2 image sharpening, it is always favorable to take advantages of the four 10-m bands and six 20-m bands to provide the richest spatial and spectral information for reliable LULC maps. In other words, the Sentinel-2 sharpening task is to merge thespatial details of its 10-m bands into its 20-m bands and generate an image with all ten multispectral bands at a 10-m spatial resolution. In order to investigate the impact of spatial resolution unification schemes on LULC classification, we compared the classification accuracy of images generated by one upscaling and six downscaling methods, including nearest neighbor interpolation, which is also used as the spatial interpolation method in upscaling, and five pansharpening algorithms, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP proposed by [PERSON], wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). However, the pixel-based classification only utilized spectral information, without considering texture and contextual information, which has not enough capability to fully evaluate the effects of image sharpening on classification [32; 33]. To compensate for this shortcoming, we added spatial features to ensure the assessment was more comprehensive and unbiased. Spatial features can be derived by several approaches; the most widely used are textural analysis and mathematical morphology [34; 35]. Texture is an effective representation of spatial relationship and contextual information. Grey-Level-Co-occurrence Matrix (GLCM) has been proved to be of great value in LULC classification on several data [36; 37; 38; 39]. Mathematical morphology is a framework which can be extended into morphological profiles (MPs), extended MPs (EMPs), attribute profiles (APs), extended APs (EAPs), extended multi-AP (EMAP) [40; 41; 42]. APs provide a multilevel characterization of the input image by the sequential application of morphological attribute filters, and it can be considered as a generalization of MPs. EAPs is computed on a few principal components (PCs) extracted by Principal Components Analysis (PCA) transformation, not only addresses the limitation of MPs in the capability of describing spatial features, but addresses the constraint of APs in the computational intensity and reduce the dimensionality of the original feature space. Thus, EAPs has been widely used to improve the accuracy of LULC classification on high resolution and hyperspectral images [43; 44]. Although the EAPs have been successfully applied to middle resolution remote sensing images, they were not investigated in Sentinel-2, as well as GLCM [45; 46]. The specific research objectives are to: (1) evaluate the effects of the two different spatial resolution unification schemes, i.e., upscaling and downscaling, on classification accuracy of Sentinel-2 imagery; (2) assess the role of image sharpening techniques in LULC classification; and (3) investigate the value of spatial features, i.e., EAPs and GLCM, in LULC classification. The remainder of this paper is organized as follows: the general situation of study area and data are presented in Section 2. Spatial resolution unification schemes and datasets, as well as the classification system, are introduced in Section 3. Section 4 illustrates and discusses the classification results. Finally, conclusions are drawn in Section 5.