## 5 Conclusions  
To retrieve surface turbidity from the LSSR product, a regional algorithm was developed and used in the Ramganga River. This investigation suggests that satellite information can be a ground-breaking device to foresee the concentration of turbidity in stream waters, and particularly in the Ramganga River. However, the distinguished models would be efficient only in the Ramganga River or rivers with comparable water quality and morphological characteristics. Nevertheless, even with the existence of a lot of ground information similar to the case in our examination, a quantitatively accurate estimation of water quality components in inland waters is a great challenge. Using the data acquired byvarious other sensors, such as Sentinel 2, Moderate Resolution Imaging Spectroradiometer (MODIS), and Gaofen-3 (GF-3),can help improve our ability to correctly estimate surface water characteristics from space.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/11/3702/s1](http://www.mdpi.com/2076-3417/10/11/3702/s1)]([http://www.mdpi.com/2076-3417/10/11/3702/s1](http://www.mdpi.com/2076-3417/10/11/3702/s1)), Figure S1a: Turbidity map of Ramganga River in 6 March 2014, Figure S1b: Turbidity map of Ramganga River in 6 March 2014, Figure S2a: Turbidity map of Ramganga River in 11 November 2014, Figure S2b: Turbidity map of Ramganga River in 11 November 2014, Figure S2c: Turbidity map of Ramganga River in 11 November 2014, Table S1: Models' summary and regression analysis statistics among turbidity concentrations and surface reflectance values for March and November 2014 (dependent variable), Table S2: Variables entered/removed from turbidity predictive models relying upon the regression method utilized for March and November 2014, Table S3: Comparison of satellites retrieved and in-situ observed turbidities values at 9 sampling sites of Ramganga River in March and November 2014 with statistical analysis for squared residual, root mean square (RMSE).  
Software: M.A. and M.Y.A.K.; Writing M.A. and M.Y.A.K.; revision and final editing: M.A., Q.M. and M.Y.A.K.; remote sensing and GIS data analysis: M.A. and Q.M.; In-situ data collection and analysis: M.Y.A.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Council for Scientific and Industrial Research (CSIR), New Delhi, grant number 19-06/2011 (i) EU-IV.  
The authors are also thankful to the National Key Research and Development Program, \"Spatial information service and application demonstration of comprehensive monitoring of urban and rural ecological environment\" (project number: 2017 YFB0503900, 2017 YFB0503905); the Major Projects of High Resolution Earth Observation Systems of National Science and Technology (05-Y30B01-9001-19/20-1); and the Science and Technology Cooperation Project of Sanya Municipal Institute, \"Non-Point Source Pollution Risk Identification and Quantitative Assessment for Surface Water Source Using Remote sensing\" (project number: 2018 YD10), for their technical support in this study. The authors are also thankful to Council for Scientific and Industrial Research (CSIR), New Delhi, and IIT Roorkee, India for giving a research fellowship (ref. no: 19-06/2011 (i) EU-IV). The authors gratefully acknowledge the comments of the reviewers and the editor, which enormously improved the presentation of the manuscript.  
The authors declare no conflict of interest.