Ammonia Nitrogen Monitoring of Urban Rivers with UAV-Borne Hyperspectral Remote Sensing Imagery

Abstract

Ammonia nitrogen (NH4-N) can cause water eutrophication and is the main oxygen-consuming pollutant in water bodies. Remote sensing methods are more macroscopic than traditional measurement methods. However, due to the weak optical characteristics of NH4-N, traditional remote sensing data cannot meet the needs of NH4-N monitoring. In response to this problem, this paper attempts to use unmanned aerial vehicles (UAV) hyperspectral imagery combined with extreme gradient boosting (XGBoost)regression algorithm to quantitatively retrieve NH4-N in urban rivers. The results show that compared with the traditional empirical semi-empirical model, the accuracy of using the XGBoost algorithm to estimate the NH4-N in the water body is significantly improved, and is consistent with the field measurement.