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Rivers carry water and material transport within certain boundary forms. Due to the difficulties of in-site measurement, limited hydrological observation stations, and the precision constraints of digital elevation model (DEM), there is a significant scarcity of information on small river cross-sections distributed in river source areas, mountainous regions, and remote areas, which hinders research on river hydrology and hydraulic processes. Since the beginning of this century, the International Association of Hydrological Sciences (IAHS) has been advocating for solutions to the challenges of hydrological prediction in ungauged basins (PUB). Despite large-scale remote sensing technology is increasingly applied to the extraction of river hydraulic parameters, the spatial resolution of satellite altimetry data is too low for small rivers (river width less that 150 m), which account for a high proportion of river networks. The National Aeronautics and Space Administration (NASA) launched the ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite in 2018. This satellite was equipped with the Advanced Topographic Laser Altimeter System (ATLAS), a photon-counting LiDAR system for the first time. The light spot (footprint) it projects onto the Earth's surface has a diameter of about 17 m, with a center-to-center distance between spots of only 0.7 m. This allows for the acquisition of photon point cloud data with smaller spots and higher density along the track. The high-density photon point cloud provided by ICESat-2 offers the possibility of extracting hydrological parameters of narrow rivers with high precision. This study focuses on the small rivers with a river width of less than 10 m in the Huangfu River basin, a first-order tributary of the middle reaches of the Yellow River, which is a data-scarce region.
A method for extracting the cross-sectional morphology of small rivers using ICESat-2 ATL03 data has been proposed. First, photons with medium to high confidence levels are selected to eliminate most of the noise. Then, a smoothing filter is applied for precise de-noising. Finally, the point cloud that has been precisely de-noised is manually edited to generate a DEM. The cross-sectional morphology of three different locations of small rivers were extracted based on DEM and compared with unmanned aerial vehicle (UAV) in-situ measurement results.
The results show that: (1) the method proposed in this study, which combines the selection of medium to high confidence point clouds with filtering denoising, can effectively remove the noise from photon point clouds, with a denoising rate above 63%; (2) the completeness and richness of ground points extracted based on ATL03 data are superior to those obtained by reclassifying ATL03 using ATL08 product; (3) the results of river cross-sections extracted based on ATL03 data are basically consistent with the UAV in-situ measurement results (R2>0.96, RMSE=0.69 m).
The research results preliminarily demonstrate the feasibility of using ICESat-2 altimetry data for extracting cross-sections of small rivers in data-scarce areas, partially supplying the three-dimensional spatiotemporal of small rivers in data-deficient areas, providing technical support for construction of three-dimensional river network across entire basins and the simulation of hydrological and hydraulic processes. This also indicates that ICESat-2 altimetry data have research prospects in obtaining hydrological parameters of rivers in data-scarce areas.
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