Effects of Spectral Variability Due to Sediment and Bottom Characteristics on Remote Sensing for Suspended Sediment in Shallow Rivers

Kwon, Siyoon; Noh, Hyoseob; Seo, Il Won; Park, Yong Sung

doi:10.2139/ssrn.4367443

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The empirical models designed in this study can be implemented in recently developed flow‐suspended sediment‐monitoring techniques to estimate Q t because the required input variables can be obtained by these techniques. For example, at the river scale, drone‐based remote‐sensing techniques have been applied to SSCs (Kwon et al., 2022a, 2022b; Kwon et al., 2023), bathymetry, and flows (Eltner et al., 2020; Legleiter & Harrison, 2019; Legleiter & Kinzel, 2021). ADCPs can be utilized for the simultaneous measurement of flow and suspended sediment (Noh et al., 2022, 2023; Son et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

View full text Add to dashboard Cite

Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (1) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (2) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size.This article is protected by copyright. All rights reserved.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Niroumand-Jadidi et al (2020) improved OBRA through the compilation of sample-specific multiple band ratio techniques for satellite-derived bathymetry (SMART-SDB), a fusion of the sample-specific k-nearest neighbor (KNN) method with OBRA. SMART-SDB was much more robust for inland and coastal waters, but the depth ranges of study sites were under 2.5 m. Kwon et al (2023a) demonstrated the applicability of the Gaussian Mixture Model (GMM) for classifying bottom types in shallow rivers. That study also emphasized the significance of considering spectral variability from various types of river substrates in hyperspectral retrievals of shallow water depth.…”

Section: Introductionmentioning

confidence: 89%

“…and 2021-08-20 to 2021-08-22 (Fall). Additionally, we selectively utilized the wavelength range between 446nm and 897nm, a range consistently identified as effective for retrieving both water depth and suspended sediment in various studies (Kwon et al, 2023a;Gwon et al, 2023;Legleiter & Harrison, 2019).…”

Section: Aviris-ngmentioning

confidence: 99%

“…To enhance the transferability and robustness of the hyperspectral retrievals, many recent studies have attempted to account for spectral variability in water environments (Jensen et al, 2019a;Kwon et al, 2023aKwon et al, , 2022bDethier et al, 2020;Niroumand-Jadidi et al, 2019). Niroumand-Jadidi et al (2020) improved OBRA through the compilation of sample-specific multiple band ratio techniques for satellite-derived bathymetry (SMART-SDB), a fusion of the sample-specific k-nearest neighbor (KNN) method with OBRA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Kwon,

Passalacqua,

Soloy

et al. 2024

Preprint

View full text Add to dashboard Cite

Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), LA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF-RFE) effectively generalizes high and low turbid cases in a single model, achieving a R² of 0.94 ± 0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non-linear relationships between depth and reflectance. Specifically, the short blue and Near-InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights the attenuation as the key process for deep-depth retrievals. The depth maps of WLD captured by this model distinctly represent the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.

show abstract

Effects of Spectral Variability Due to Sediment and Bottom Characteristics on Remote Sensing for Suspended Sediment in Shallow Rivers

Cited by 2 publications

References 0 publications

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Contact Info

Product

Resources

About