Monitoring the Concentration of Dissolved Inorganic Nitrogen and Phosphorus at the Sea Surface Using a Hyperspectral Image—A Case Study of Sheyang Estuary, Yellow Sea

The concentrations of DIN and DIP are important indicators in an offshore ecosystem; although they do not have optical activity, their concentrations are affected by optically active substances, such as sediment, chlorophyll, and dissolved organic matter, an association that is especially close in coastal waters. This study aimed to identify this relationship to provide a theoretical basis for using remote sensing to monitor DIN/DIP concentrations. This study first used correlation analysis to analyze the relationship between water quality indicators and the field-measured spectrum in the Sheyang estuary. The results show a strong positive correlation between the DIN and DIP concentrations and spectrum in near-infrared range, similar to that between the suspended sediment concentrations and spectrum; this indicates a close relationship between DIN/DIP concentrations and sediment concentration in this sea area. Traditional regression models for DIN and DIP concentrations were constructed using the sensitive bank factors of a Hyperion image. By comparing the physical meaning of the factors and the precision and stability of the models, the quadratic model established by the ratio factor of 45th and 10th bands was selected as the DIN concentration inversion model, the quadratic model established by the ratio factor of the 45th and 9th bands was selected as the DIP inversion model, and the inversion results of the image conformed to the actual distribution pattern of DIN and DIP concentrations. In order to fully utilize the spectral information of the Hyperion data, the model coupled using partial least squares (PLS) and support vector machine (SVM) was used to construct regression models of DIN and DIP concentrations. By comparing the standardized coefficients of PLS regression, the 8~16th bands and 37~57th bands of the Hyperion image were selected; all these bands were extracted as two orthogonal components to construct the SVM regression model. Finally, the parameter combinations of radial basis model with C = 10, γ = 0.05, and ε = 0.1 and C = 1, γ = 0.1, and ε = 0.001 were determined as the inversion models for DIN and DIP concentrations, respectively. The prediction accuracy of the models was significantly improved compared to the traditional regression models, and the inversion results were superior to those of the traditional regression models, demonstrating the potential of this algorithm in hyperspectral image modeling.