Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Abstract

Abstract: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.

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection

“…Results show that MNDWI performs equally to NDWI 2 for water bodies mapping although Du et al [14] indicates a better discrimination of water using MNDWI. Additionally, several single band indices such as Green, NIR wide , SWIR, NIR narrow and the three Red-edge bands perform equally with less than 5% classification error probability ( Figure 11).…”

“…For the water bodies' detection, Du et al [14] used NDWI 2 and MNDWI which is equivalent to the NHI in terms of information as it is the mathematical opposite. Results show that MNDWI performs equally to NDWI 2 for water bodies mapping although Du et al [14] indicates a better discrimination of water using MNDWI.…”

“…Landsat-8 acquisitions are available from March 2013; Sentinel-2A acquisitions are available from end of November 2015. Thanks to its state-of-the-art specifications, Sentinel-2 [12,13] was designed for a variety of land monitoring applications such as water detection [14], mapping built-up areas [15] and crop type and tree species identification [16]. In addition to its spatial resolution, its payload offers thirteen spectral bands from Blue to SWIR, including Red-edge bands which have already proved to be useful for forest stress monitoring [17], land use and land cover mapping [18,19] and biophysical variable retrieval [20][21][22][23].…”

Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features

“…Sentinel-2 standard level-1C (L1C) products are available freely from the Copernicus Scientific Data Hub website as Top-of-Atmosphere (TOA) reflectance orthoimages in terms of elementary granules of fixed size. The granules, also called tiles, are the minimum indivisible partition of a product in 100 × 100 km 2 with UTM/WGS84 projection [7]. The whole watershed is over the coverage of one tile, and two tiles acquired on 28 February 2017 with the center position coordinates of 31°6′45′′N, 119°40′20′′E and 30°12′39′′N, 119°38′52′′E, respectively, were used for subsequent processing.…”

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

“…Although these indices have proven effective in labeling special ground components [29][30][31], crucial problems can arise when they are applied to urban environments. The first problem is that most spectral indices are designed to highlight only a single land-cover type (e.g., vegetation or built-up area) and cannot differentiate among other land-cover types.…”