Evaluating the Performance of Sentinel-1A and Sentinel-2 in Small Waterbody Mapping over Urban and Mountainous Regions

Abstract

Accurate waterbody mapping can support water-related environment monitoring and resource management. The Sentinel series satellites provide high-quality Synthetic Aperture Radar (SAR) and optical observations that are commonly used in waterbody mapping. However, owing to the 10-m spatial resolution of Sentinel data, previous studies mostly focused on the mapping of large waterbodies. In this work, we evaluated the performance of small waterbody mapping over urban and mountainous regions with two datasets, the average annual VH backscatter coefficients (VHavg), derived from the Sentinel-1A series, and the Modified Normalized Difference Water Index (MNDWI), derived from cloud-free Sentinel-2. A proven framework of waterbody mapping based on watershed segmentation and noise reduction was employed to assess the performance of the two datasets in waterbody identification. The validation was performed by comparing their results with 1-m spatial resolution reference waterbody data. Assessment metrics, including Precision, Recall, and F-measure, were employed. Results showed that: (1) the MNDWI outperformed the VHavg by 9 percentage points of the F-measure; (2) there was more room for results of VHavg to improve the accuracy through a combination with noise reduction; and (3) the potential smallest identifiable waterbody area (recall rate larger than 0.8) was larger than 104 m2.

Flooded cropland mapping based on GF-3 and Mapbox imagery using semantic segmentation: a case study of Typhoon Siamba in western Guangdong in July 2022

“…Previous studies have shown that the using watershed segmentation to extract waterbody has better mapping results than the single threshold method [1]. The watershed algorithm is a region extraction algorithm based on morphology, which grayscales the image and then uses the analysis of geographic morphology to segment the image gradient.…”

“…To this end, some studies propose to combine the above methods with change detection or time series analysis to determine the flooding range. However, practice has shown that flood extraction based on SAR images is susceptible to other confusing features such as shadows from mountains/buildings and flat surfaces [1]. Although many studies use reference data (such as DEM/NDVI) to remove noise, there are still many noises that cannot be removed, and it is easy to remove the correct waterbody.…”

Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin by Multitemporal SAR Data

“…The accuracy of flood detection in VV polarization is higher than that in VH polarization [20], [21], so we selected the SAR images in VV polarization. The L2A products obtained by Sentinel-2 were processed to generate the modified normalized difference water index 3 > ID NUMBER < (MNDWI) [22] for validation. Fig.…”

Mapping Water Bodies and Wetlands from Multispectral and SAR Data for the Cross-Border River Basins of the Polish–Ukrainian Border

“…Scientists have successfully applied remote sensing technologies in hydrogeological, environmental, and geological research using optical and radar data from various satellites: Sentinel-1 [1][2][3][4][5][6][7][8][9][10]; Sentinel-2 [2,3,[7][8][9][10][11][12][13][14][15][16][17][18][19][20]; Landsat-1, -2, and -3--8 [10,12,14,15,[21][22][23][24][25][26][27][28][29][30][31][32][33]; MODIS (Terra/Aqua) [24,[34][35][36][37][38][39][4...…”