## 5 Conclusions  
This paper assesses the effects of different spatial resolution unification schemes as well as spatial features on LULC classification accuracy using Sentinel-2 multi-resolution and multi-spectral images. For this purpose, one upscaling and six downscaling algorithms were used to generate seven data sets for classification. The spatial features of EAPs and GLCM were added to ensure an unbiased assessment by compensating for the shortcomings of image sharpening and spatial information loss in pixel-based classification. Their improvements in classification accuracy were investigated for the first time for Sentinel-2 imagery. For Sentinel-2, downscaling based on even the most straightforward nearest neighbor resampling has an overwhelming superiority over upscaling in LULC classification. Therefore, we recommend the use of downscaling to unify the spatial resolution in data preprocessing for LULC classification of Sentinel-2. For downscaling techniques, image sharpening has little effect on overall accuracy than spatial interpolation, and some of them even significantly underperform the resampling and are difficult to be applied. However, image sharpening algorithms outperform resampling in some specific classes, e.g., the two MRA-based methods, i.e., WTF and HPF, have obvious advantages than resampling in water and road, but at the cost of a lower accuracy in shrub, bare land, and built-up area. For image sharpening, the NNDiffusion and PANSHARP based on statistics achieved a similar accuracy as the GS method based on CS, and both of them underperform WTF and HPF, which are based on MRA. Figure 7: Differences between extended attribute profiles (EAPs) and Grey-Level-Co-occurrence Matrix (GLCM) on class accuracy. Columns above \(x\)-axis stand for adding GLCM achieved a higher accuracy than EAPs on their corresponding classes, and those below \(x\)-axis are the opposite. Both EAPs and GLCM can significantly improve the LULC classification accuracy of Sentinel-2 imagery. The best accuracy can be achieved by adding both of them, similar to the findings in the previous literature. Although their improvements on overall accuracy are similar, the effectiveness to specific classes differ greatly. GLCM has a better performance in built-up area, water, and road, whereas EAPs contribute to a higher improvement in shrub and bare land. In agricultural land and forest, which already achieved a high precision, their effects are similar. Although the spatial features have a significant effect on the accuracy of some specific classes, considering the computational complexity and the limited benefit of image sharpening, the image downscaled by nearest neighbor interpolation solely based on spectral bands can already meet the requirement of regional LULC applications. The EAPs and GLCM can be integrated with spectral bands separately or together for the applications that have a specific requirement to some certain classes. The most accuracy LULC map achieved by the downscaled image by HPF with the spatial features of EAPs and GLCM as shown in Figure 8.  
Our further study will focus on establishing a hierarchical classification system that aims at discriminating more vegetation species, where the 20-m bands of Sentinel-2 (e.g., the three unique red-edge bands) have more contributions, to evaluate the potential of image sharpening in classifying different crops, trees, and other vegetation species. This work is supported by Natural Science Foundation of China (No. 41631176). Thanks to the anonymous reviewers and the editor for their constructive comments and helpful suggestions, which have greatly improved this manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] and [PERSON] provided many helpful suggestions and modified this manuscript very seriously; [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest.