Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies’ mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

Extracting land cover change (LCC) information at the subpixel scale is important when coarse-resolution remotely sensed images are used for change detection. Although fraction images derived from soft-classification technologies can be used for subpixel LCC detection, the spatial distribution of changed subpixels within each coarse-resolution pixel cannot be provided. This letter presents a subpixel LCC mapping (SLCCM) algorithm, aiming to predict the spatial pattern of LCC at the subpixel scale between bitemporal images through comparing the former high-resolution land cover map and the latter fraction images derived from the coarse-resolution image. The resulting subpixel LCC map is determined by the spatial dependence principle and an LCC rule in each mixed pixel. The proposed algorithm was evaluated with simulated and real images, and the results showed the effectiveness of the proposed method for SLCCM.

Super-resolution mapping (SRM) is a technique to produce a land cover map with finer spatial resolution by using fractional proportion images as input. A two-step SRM approach has been widely used. First, a fine-resolution indicator map is estimated for each class from the coarse-resolution fractional image. All indicator maps are then combined to create the final fine-resolution land cover map. In this letter, three popular interpolation methods, Inverse Distance Weighted (IDW), Spline and Kriging, as well as four indicator map combination strategies, including the maximal value strategy and the sequential assignment strategy with and without normalization, were assessed. Based on the application to two simulated images, the performance of all SRM algorithms was assessed. The results show that the two-step SRM approach can obtain smoother land cover maps than hard classification. An increase in zoom factor results in the appearance of numerous small patches and linear artefacts in the SRM results. The accuracies of Spline and Kriging are similar and are both higher than that of IDW. The maximal value strategy can generate a smoother land cover map than the sequential assignment strategy in most cases, and a normalizing indicator value has a mixed effect on the result.