Remote sensing-based water quality assessment for urban rivers: a study in linyi development area

  • Sheng Miao
  • Chao LiuEmail author
  • Binjie Qian
  • Qun Miao
Water Environmental Pollution and State of the Art Treatment Technologies


Nowadays, urban rivers play an important role in city development and make great contributions to urban ecology. Most urban rivers are the drinking water sources and water quality is extremely critical. The current assessment method in national standard of China has multiple limitations; therefore, this paper introduces an advanced assessment, that is, Canadian Water Quality Index (CWQI). This method can help to provide comprehensive and objective water quality assessment for the urban rivers. Moreover, CWQI can prevent waste of the water resource, since current assessment is pessimistic and tent to underestimate water samples to a lower grade. Linyi development area is selected as study region and CWQI method is applied to assess two major urban rivers within the area. The water monitoring data from 2014 to 2017 is acquired in 24 parameters. Since the CWQI calculation is still based on traditional water quality measurement in parameters, there will be a huge cost when increasing research scale and accuracy. In this paper, remote sensing technique is employed to develop models of CWQI scores from satellite data. By utilizing 23 selected monitoring instances and matching satellite data, linear regression analysis shows that red band data has highest correlation with CWQI in both two urban rivers in the study region. In addition, two testing datasets with five instances for each river are used to validate the RS-based CWQI models and the results show that testing datasets can be fitted well. With the models, CWQI distribution diagrams are generated and assist both spatial and temporal analysis. Experimental results show that the proposed approach can indicate actual water quality pattern which is validated by field visit. The proposed approach in this paper has satisfying effectiveness and robustness.


Remote sensing Water quality index Urban rivers Spatial temporal analysis 



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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Qingdao UniversityQingdaoChina
  2. 2.Qingdao University of TechnologyQingdaoChina

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