Satellite Video Remote Sensing for Estimation of River Discharge

Masafu, Christopher; Williams, Richard; Hurst, Martin D.

doi:10.1029/2023gl105839

Cited by 6 publications

(3 citation statements)

References 55 publications

(72 reference statements)

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“…Satellite data can yield measurements of water surface elevation (and by extension D) and river W, S, and V (Table 1), which individually or in combination provide a basis for estimating discharge. Use of satellitemeasured water surface velocity to estimate discharge has been demonstrated (e.g., Beltaos & Kääb, 2014;Emery et al, 1986;Legleiter & Kinzel, 2021;Masafu et al, 2023) and may improve with increasing availability of satellite high-resolution video and rapid-repeat imagery.…”

Section: Satellite-based Discharge Estimationmentioning

confidence: 99%

Siting Considerations for Satellite Observation of River Discharge

Eggleston,

Mason,

Bjerklie

et al. 2024

Water Resources Research

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With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite‐based discharge measurements. Satellite‐based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite‐based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground‐truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross‐sectional area. A separate database of river surface slope and discharge time‐series was developed from paired continuous monitoring stations to examine slope‐discharge correlations. Results show that discharge correlates most strongly with velocity, cross‐sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach‐specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.

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Section: Satellite-based Discharge Estimationmentioning

confidence: 99%

Siting Considerations for Satellite Observation of River Discharge

Eggleston,

Mason,

Bjerklie

et al. 2024

Water Resources Research

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“…Many researchers have focused on optical sensors to estimate water flow [9,17,[34][35][36][37][38][39]. While conceptually similar to the altimeter method that is described above, measurements that are made with optical sensors are derived by the satellites themselves.…”

Section: Introductionmentioning

confidence: 99%

“…Building on the challenges associated with optical sensors, a recent study [35] explored an alternative approach using satellite videos for river discharge estimation. This method utilizes large-scale particle image velocimetry (LSPIV) techniques to estimate river flow.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Ziadi,

Chokmani,

Chaabani

et al. 2024

Remote Sensing

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Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

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An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

Legleiter,

Kinzel

2024

River Research & Apps

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Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

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Satellite Video Remote Sensing for Estimation of River Discharge

Cited by 6 publications

References 55 publications

Siting Considerations for Satellite Observation of River Discharge

Siting Considerations for Satellite Observation of River Discharge

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

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