Effects of spectral variability due to sediment and bottom characteristics on remote sensing for suspended sediment in shallow rivers

Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers

“…In contrast, YL demonstrates a high reflectance variation, particularly near the red wavelength, leading to high visibility. The spectral characteristics of these sediments in dry and wet conditions can be found in Kwon et al (2023b) [21]. The sediment density and particle size are also substantially different.…”

“…In specific, turbulence near the riverbed causes vegetation to move randomly, and this phenomenon can induce high levels of noise in the total reflectance, whereas a sand bed is relatively stable against turbulent flow. Thus, the non-fixed bottom condition of a vegetated bed results in temporally non-stationary bottom reflectance, thereby leading to low R 2 values in estimating the concentration of both QS and YL cases, even if the YL case shows slightly higher R 2 values due to its inherently brighter characteristics [21]. Nevertheless, the sediment type is less sensitive to the correlation between the SSC and reflectance compared to the bed type.…”

“…To retrieve the SSC with varying sediment and bed types from remotely captured HSIs and selected multispectral images (MSIs), we employ an optimal band ratio analysis (OBRA) to analyze the relationships between the SSC and reflectance. OBRA is the method widely used for interpreting the optical attributes of a target variable and determining its appropriate spectral bands from multispectral data [21,33,34]. OBRA is encoded to extract effective spectral bands from HSI data by iteratively comparing the coefficient of determination (R 2 ) between the SSC and band ratios of all combinable spectral band pairs.…”

“…Some previous field-based RS research also reported that a bottom reflectance induced by vegetation cover substantially affects the spectral characteristics of a water body in shallow water, thereby complicating the interaction between reflectance and target matters [20,36]. Kwon et al (2023b) [21] investigated the impact of sediment and bottom characteristics on the RS of SSC by decomposing the fraction of the sediment signal and bottom signal from total reflectance. This study demonstrated that the fraction of the bottom signal is much larger than that of the sediment signal in the vegetated bed, and this behavior decorrelated the relationship between the total reflectance and measured SSC in the shallow and vegetated rivers.…”

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

“…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).…”