Spatio-Temporal Distribution of Dissolved Inorganic Nitrogen in the Changshan Islands Archipelago Based on a Multiple Weighted Regression Model Considering Spatial Characteristics

Abstract

Ammonia nitrogen (NH4-N), nitrite nitrogen (NO2-N), and nitrate nitrogen (NO3-N) are important nutrients for maintaining the ecological balance of seawater archipelagos. Obtaining the concentrations of the three nitrogenous compounds simultaneously can allow us to comprehensively analyze nitrogen cycling in archipelago waters, which is beneficial to the ecological protection of both agriculture and fisheries. The existing studies have usually considered a single nitrogen compound or dissolved inorganic nitrogen (DIN), which can only identify the water quality but cannot comprehensively judge the water purification situation or the toxicity of the nitrogen compounds in the water. In the process of constructing an inversion model, only the specific bands of remote sensing imageries used in training/learning are directly related to the actual measured values, ignoring the fact that the specific bands contain information on water quality parameters is different that would affect the fitting accuracy. Furthermore, the existing empirical models and machine learning models have not yet been applied to high-resolution inversion in archipelago waters with active fishing activities. In view of this, we constructed a multiple weighted regression model considering spatial characteristics (S-WSVR) to simultaneously retrieve the distribution of NH4-N, NO2-N, and NO3-N in archipelagic waters. By using the S-WSVR model and considering the complexity of the spatial distribution of the three nitrogen compounds in the mesoscale archipelagic waters, longitude and latitude were added to the experimental dataset as spatial features to fit the nonlinear spatial relationships. Meanwhile, a multivariate weighting module based on the Mahalanobis distance was integrated to calculate the contribution of the characteristic bands and improve the inversion accuracy. The S-WSVR model was applied in the water of Changshan Islands, China, with a retrieval resolution of 30 m, and the r-values of the three nitrogen compounds achieved 0.9063, 0.8900, and 0.9755, respectively. Notably, the sum of the three nitrogen compounds has an r-value of 0.9028 when compared with the measured DIN. In addition, we obtained the Landsat 8 characteristic bands for the three nitrogen compounds and plotted the spatial distributions of the nitrogen compounds in spring and autumn from 2013 to 2022. By analyzing the spatio-temporal variations, it was apparent that the three nitrogen compounds are controlled by human activities and river inputs, and the anoxic discharge of the Yalu River has a strong influence on NO2-N content. Therefore, the accurate estimation in this study can provide scientific support for the protection of sensitive archipelago ecosystems.