Abstract
The fine extraction of water boundaries is of great significance for water resource monitoring, water environment monitoring, and flood prevention. MODIS images are widely used for water extraction due to their high temporal resolution, wide coverage, gratuity, and long observation period. However, owing to their low spatial resolution, the water boundary results are often blurred. It is difficult to extract water boundaries accurately. The subpixel mapping algorithm can solve this problem. In this article, Dongting Lake and its surroundings are adopted as the experimental area. The digital elevation model (DEM) is used to modify the subpixel/pixel spatial attraction model (SPSAM) mapping results. The proposed algorithm is referred to as the DEM-modified SPSAM (D-MSPSAM). Based on the visual results of the two sets of experiments, the modified results suitably maintain the spatial details of the water, and many of the underestimations caused by the similarity of the spectral characteristics of the surroundings to those of the water have been corrected. In this paper, the accuracy of Landsat-8 water extraction is used as a reference. Based on the quantitative results, the D-MSPSAM method has a higher extraction accuracy than the traditional threshold method, and the accuracies of the extraction for high water and low water have been increased by 3.56 percentage points and 2.77 percentage points, respectively. Furthermore, these results also confirm the potential application of DEM data for flood submergence extraction and provide new ideas for the improvement of the subpixel mapping model. The proposed method can accurately generate water distribution maps in a practical and economical way.
Highlights
Water boundaries, which are the junctions where water and land meet, include lakeshores, river banks, coasts and flood boundaries
Two 30-m-resolution Landsat 8 Operational Land Imager (OLI) images acquired on the same day were selected as a reference for the water extraction results, and 30m-resolution ASTGTM2 digital elevation model (DEM) data were selected as subpixel mapping auxiliary correction data [28]
The D-MSPSAM method can remove irrigated paddy fields from the classified image and more accurately distinguish areas where the flood propagates. By excluding these irrigated fields, the D-MSPSAM method may be less suited when monitoring all areas under water, for instance in research on evapotranspiration and agriculture
Summary
Water boundaries, which are the junctions where water and land meet, include lakeshores, river banks, coasts and flood boundaries. The low spatial resolution of MODIS images causes mixed pixels, which greatly limits the accuracy of MODIS water extraction and the application of MODIS in the field of water remote sensing. Verhoeye et al studied the mathematical model of the spatial correlation theory and transformed subpixel mapping into a linear optimization problem On this basis, Mertens et al proposed a genetic algorithm (based on the BP neural network (BPNN) [15], [16]) called the subpixel/pixel spatial attraction model (SPSAM) [17], [18]. Additional spatial distribution features describing the types of objects inside mixed pixels can be included Auxiliary information, such as DEM elevation data, high-resolution remote sensing images, and feature boundaries, can be adopted as a constraint to improve the accuracy of subpixel mapping [19]–[21]. This method introduces DEM data and uses elevation values to determine whether a subpixel is a submerged pixel, the traditional subpixel mapping is modified, and a higher accuracy for the submerged subpixel mapping of water bodies is achieved
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