Can Surface Water Color Accurately Determine Sediment Concentration and Grain Size? A Hyperspectral Imaging Study

Abstract

The characteristics of suspended sediments determine the water color, and remote sensing methods have been developed to leverage this physics to determine sediment concentration and size. However, current measurement practices rely on empirical correlations, which have only been tested for a limited range of particle conditions. This gap prevents their applicability in the field. To address the issue, this study analyzes hyperspectral spectra across various wavelength bands to characterize spectral signatures of different sediment sizes and concentrations. The results reveal inflection points of the light scattering of suspended sediment solution depending on particle concentration and sizes: the light scattering positively correlates with a low concentration but negatively correlates with a high concentration, while it negatively correlates with particle size for low concentrations but positively correlates for high concentrations. Sensitivity analyses indicate increased responsiveness to concentration changes at low concentrations and a higher sensitivity to particle size changes at both low and high concentrations. Machine learning models were tested for simulated satellite bands, and it was found that existing machine learning models are limited in reliably determining sediment characteristics, reaching an R-square of up to 0.8 for concentration and 0.7 for particle size. This research highlights the importance of selecting appropriate wavelength bands in the appropriate range of sediments and the need to develop advanced models for remote sensing measurements. This work underscores hyperspectral imaging’s potential in environmental monitoring and remote sensing, revealing the complicated physics behind water color changes due to turbidity and informing next-generation remote sensing technology for turbidity measurements.