Abstract
Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination (R2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher (R2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.
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Acknowledgements
Authors would like to acknowledge the Hong Kong Environmental Protection Department (EPD) for providing the in-situ salinity data and the U.S. Geological Survey for providing Landsat TM images. The National Key Research and Development Program of China (No. 2016 YFC1400901) has sponsored this research.
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Nazeer, M., Bilal, M. Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity. J. Ocean Univ. China 17, 305–310 (2018). https://doi.org/10.1007/s11802-018-3380-6
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DOI: https://doi.org/10.1007/s11802-018-3380-6