Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques

Essam Sharaf El Din; Yun Zhang

doi:10.1117/1.JRS.11.046008

27 October 2017 Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques

Essam Sharaf El Din, Yun Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046008 (October 2017). https://doi.org/10.1117/1.JRS.11.046008

Abstract

Traditional surface water quality assessment is costly, labor intensive, and time consuming; however, remote sensing has the potential to assess surface water quality because of its spatiotemporal consistency. Therefore, estimating concentrations of surface water quality parameters (SWQPs) from satellite imagery is essential. Remote sensing estimation of nonoptical SWQPs, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and dissolved oxygen (DO), has not yet been performed because they are less likely to affect signals measured by satellite sensors. However, concentrations of nonoptical variables may be correlated with optical variables, such as turbidity and total suspended sediments, which do affect the reflected radiation. In this context, an indirect relationship between satellite multispectral data and COD, BOD, and DO can be assumed. Therefore, this research attempts to develop an integrated Landsat 8 band ratios and stepwise regression to estimate concentrations of both optical and nonoptical SWQPs. Compared with previous studies, a significant correlation between Landsat 8 surface reflectance and concentrations of SWQPs was achieved and the obtained coefficient of determination (R2)>0.85. These findings demonstrated the possibility of using our technique to develop models to estimate concentrations of SWQPs and to generate spatiotemporal maps of SWQPs from Landsat 8 imagery.

Citation Download Citation

Essam Sharaf El Din and Yun Zhang "Estimation of both optical and nonoptical surface water quality parameters using Landsat 8 OLI imagery and statistical techniques," Journal of Applied Remote Sensing 11(4), 046008 (27 October 2017). https://doi.org/10.1117/1.JRS.11.046008

Received: 20 July 2017; Accepted: 3 October 2017; Published: 27 October 2017

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