Retrieving TN and TP Concentration of Urban River From High Resolution IKONOS Multispectral Imagery

Abstract:

Total nitrogen (TN) and Total phosphorus (TP) are widely known as two important indexes to measure China urban rivers, and the technique of remote sensing plays an important role in quantitatively monitoring the dynamic change and timely grasping the status of urban rivers. Taking Wen-rui Tang River as examples, this paper develops both multiple regressions (MR) model and artificial neural networks (ANN) model to estimate TN and TP concentration from high resolution IKONOS image data and in situ water samples collected concurrently with satellite overpass. By analyzing determination coefficients (R²) and relative root mean square error (RMSE), it is found that the measured and estimated values of both MR and ANN models are in good agreement (R²>0.85 and RMSE<2.50), and the estimated accuracy using ANN model is better (R²>0.86 and RMSE<0.89). The results also present the potential of high resolution IKONOS multispectral imagery to apply to urban rivers. The spatial distribution maps of TP and TN concentration generated by ANN model present apparent spatial variations and inform the decision makers of water quality variations in Wen-rui Tang River. The approach developed in this study proves to be effective and has the potential to be applied over urban rivers for water quality monitoring.