River Water Level and Water Surface Slope Measurement From Spaceborne Radar and LiDAR Altimetry: Evaluation and Implications for Hydrological Studies in the Ganga River

Dhote, Pankaj R.; Agarwal, Ankit; Singhal, Gaurish; Calmant, Stephane; Thakur, Praveen K.; Oubanas, Hind; Paris, Adrien; Singh, Raghavendra P.

doi:10.1109/jstars.2024.3379874

Cited by 1 publication

(1 citation statement)

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“…Satellite altimetry is one of the most powerful measurement techniques for solving the major challenges in river hydraulic characterisation, such as water depth, channel bathymetry, flow velocity and discharge [1][2][3][4][5]. It can be used to measure water surface elevation (WSE) directly by calculating the time it takes for the emitted signal to travel to the ground and back to the satellite sensor [6]. Overall, it provides an enhanced spatiotemporal coverage, reducing the time and cost of data collection compared to traditional in situ measurements.…”

Section: Introductionmentioning

confidence: 99%

Refining ICESAT-2 ATL13 Altimetry Data for Improving Water Surface Elevation Accuracy on Rivers

Chen,

Liu,

Ticehurst

et al. 2024

Remote Sensing

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The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect solely with water. It is acknowledged that the ATL13 product has noise elevations of the adjacent land, resulting in biased high mean WSEs at VSs. Earlier studies have relied on human intervention or water masks to resolve this. Both approaches are unsatisfactory solutions for large river basins where the issue becomes pronounced due to many tributaries and meanders. There is no automated procedure to partition the truly representative water height from the totality of the along-track ICESAT-2 photon segments (portions of photon points along a beam) for increasing precision of the mean WSE at VSs. We have developed an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparing the ATL13-derived WSEs with direct water level observations at 10 different gauging stations on 37 different dates along the Lower Murray River, Australia. The concordance between the two datasets is significantly high and without detectable bias. In addition, we evaluated the effects of four methods for calculating the mean WSEs at VSs after auto-segmentation processing. Our results reveal that all methods perform almost equally well, with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189 m) and MAE (0.130–0.142 m). We also found that the R2, RMSE and MAE are better under the high flow condition (0.999, 0.124 and 0.111 m) than those under the normal-low flow condition (0.997, 0.208 and 0.160 m). Overall, our auto-segmentation method is an effective and efficient approach for deriving accurate mean WSEs at river VSs. It will contribute to the improvement of ICESAT-2 ATL13 altimetry data utility on rivers.

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