Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

River width can reveal the water extent and water flow on the earth's surface. Remote sensing facilitates river width extraction at large scales in an automatic way. This letter proposes a new method named GrabRiver that implements whole-process automation from image preparation to river width calculation. It also, for the first time, develops river graph as the simultaneous river topology for width extraction, combining river planform morphology and network. Three major steps are proposed: 1) mapping and connecting rivers. The Multispectral Water Index (MuWI) is used to produce high-accuracy water maps from imagery, whereby specialized algorithms are used to reduce the impacts from nonriver water (lakes, reservoirs, wetlands) and on-channel objects (bridges, dams, ships) and to enforce river connectivity; 2) Constructing the river graph. A connected river map is skeletonized into the graph while maintaining georeferences as properties of edges and nodes in the river graph, and it is followed by river graph pruning to remove false and redundant river tributaries in the topologic structure; and 3) Measuring river widths. The cross-sectional measure is conducted on the river-reach (graph edge) basis, where orthogonals to centerlines are determined by the bounding geometry. In our experiments, the output results of GrabRiver are consistent with the reference river widths (R² = 0.98 in the mean width validation and R² = 0.91 in the transient width validation). Despite that GrabRiver is a promising method, random uncertainty in water mapping is identified as the major source of width measurement errors. The outputs of GrabRiver will be applicable in fluvial analysis and satellite-derived discharge estimates.