Abstract
AbstractRiver wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery, and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the subpixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by superresolution mapping (SRM). In the SRM analysis, a deep convolutional neural network is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the convolutional neural network‐based SRM model can effectively estimate subpixel scale details of rivers and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery.
Highlights
Rivers and streams are key routes for water movement and play a major role in local‐ to global‐scale hydrological and biogeochemical cycles (Allen & Pavelsky, 2018; Downing et al, 2012)
The proposed approach is assessed in two experiments, with the results demonstrating that the convolutional neural network‐based superresolution mapping (SRM) model can effectively estimate subpixel scale details of rivers and that the accuracy of River wetted width (RWW) estimation is substantially higher than that obtained from the use of a conventional hard image classification
A Landsat OLI image (Figure 3b) acquired on 22 December 2016 was obtained via the United States Geological Survey (USGS) Earth Explorer, and the reference fine‐resolution river map with a spatial resolution of 30 m was produced from the Landsat OLI image with a threshold approach based on the Modified Normalized Difference Water Index (Xu, 2006; Figure 3c). 3.1.2
Summary
Rivers and streams are key routes for water movement and play a major role in local‐ to global‐scale hydrological and biogeochemical cycles (Allen & Pavelsky, 2018; Downing et al, 2012). Remote sensing‐based approaches to RWW estimation typically require the production of a map depicting rivers by manual digitization or automatic image processing methods (Pavelsky & Smith, 2008). A variety of remotely sensed data sets (images) is available for river mapping (Priestnall & Aplin, 2006). These images have different temporal and spatial resolutions and are suitable for a range of application requirements.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
