Using MODIS remote sensing data for mapping the spatio-temporal variability of water quality and river turbid plume

• Published in: Journal of coastal conservation Vol. 21; no. 6; pp. 939 - 950
• Main Authors: Hamidi, Sajad Ahmad, Hosseiny, Hossein, Ekhtari, Nima, Khazaei, Bahram
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-12-2017; Springer Netherlands; Springer Nature B.V
• Subjects: AQUA spacecraft; Coastal Sciences; Coastal zone; Coefficients; Correlation coefficient; Correlation coefficients; Data; Earth and Environmental Science; Empirical equations; Evaluation; Frameworks; Geography; Image quality; Imagery; Imaging techniques; In situ measurement; Lakes; Land pollution; Mathematical analysis; Mathematical models; MODIS; Nature Conservation; Oceanography; Pollutants; Pollution studies; Reflectance; Remote monitoring; Remote sensing; Remote Sensing/Photogrammetry; Rivers; Satellites; Spectroradiometers; Suspended sediments; Temporal variations; Turbidity; Water pollution; Water quality; Water quality measurements; Fox River; Water quality monitoring; Turbidity; Lake Michigan; Environmental remote sensing; River plume; Modis; Total suspended sediment; Fox River
• ISSN: 1400-0350; 1874-7841
• DOI: 10.1007/s11852-017-0564-y

Fox River is the main source of land-based pollutants that flows into the southern Green Bay of Lake Michigan. Evaluation of water quality is normally based on time consuming and expensive in situ measurements. Remotely sensed data is an efficient alternative for field monitoring because of its spatial and temporal coverage. In this study, remote sensing imagery combined with in situ measurements of water quality were used to estimate an empirical relationship between water surface reflectance and water quality parameters including water turbidity and Total Suspended Sediment (TSS). Surface reflectance values is obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The empirical equations were derived from data over summers 2011–13 and show high correlation coefficients of equal to 0.83 and 0.87 for TSS and turbidity respectively. The validity of the proposed equations was tested for summer 2014 data. The NRMSE for prediction of measured data by the proposed equations are 0.36 and 0.3 for TSS and turbidity. Remotely sensed data was also used to produce water quality maps to improve our understanding of the spatiotemporal variations of Fox River turbid plume. The proposed approach can be extended to other coastal regions of Great Lakes and provide a framework to study pollution transportation in coastal areas.