Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters

Abstract

the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment. This experiment was conducted with four sediment types injected into an experimental meandering channel divided into two reaches with submerged vegetation and a natural sand bottom. We obtained high-resolution hyperspectral images using unmanned aerial vehicles (UAVs) and measured the in situ suspended sediment concentration using laser diffraction sensors. Based on optical similarity, we used CMR-OV to divide the hyperspectral dataset into several clusters. Then, we built separate RFR models for each cluster using the corresponding spectral bands that were selected using recursive feature elimination (RFE). Thus, we found that the proposed CMR-OV yielded superior results compared to the conventional RFR model, decreasing the total error score by 10.81%. The optical spectral bands of each cluster were distinguished from each other, indicating that the datasets that were spectrally discriminated from clustering enhanced the performance of the estimator. By comparing the clustered spectral dataset and physical factors, we proved the bottom type was the most critical factor in separating the clusters, even though the variability in the sediment properties also induced substantial spectral changes. Our findings demonstrated that CMR-OV accurately reproduced the spatiotemporal distribution of suspended sediment under optically complex conditions by addressing the heterogeneity of bottom reflectance in shallow water.