Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery

Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying the concentrations of the optically active water quality parameters, for parameters such as chlorophyll-a (Chla), cyanobacteria, and colored dissolved organic matter (CDOM). For the optically inactive parameters, such as the permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), ammoniacal nitrogen (NH3–N), and heavy metals, it is difficult to estimate the concentrations directly, and the traditional indirect estimation models cannot meet the accuracy requirements, especially in heavily polluted inland waters. In this study, 60 water samples were collected at a depth of 50 cm from the Guanhe River in China, at the same time as the airborne data acquisition. We also developed and investigated two deep learning based regression models—a pixel-based deep neural network regression (pixel_DNNR) model and a patch-based deep neural network regression (patch_DNNR) model—to estimate seven optically inactive water quality parameters. Compared with the partial least squares regression (PLSR) and support vector regression (SVR) models, the deep learning based regression models can obtain a superior accuracy, especially the patch_DNNR model, which obtained a superior prediction accuracy for all parameters, with the prediction dataset coefficient of determination (Rp²) and the residual prediction deviation (RPD) values being greater than 0.6 and 1.6, respectively. In addition, thematic maps of the water quality classification results and water parameter concentrations were generated and the overall water quality and pollution sources were analyzed in the study area. The experimental results demonstrate that the deep learning based regression models show a good performance in the feature extraction and image understanding of high-dimensional data, and they provide us with a new approach for optically inactive inland water quality parameter estimation.