A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

Authors

  • M. V. Japitana College of Engineering and GeoSciences, Caraga State University, Philippines | School of Engineering, University of San Carlos, Philippines http://orcid.org/0000-0001-7921-1270
  • M. E. C. Burce School of Engineering, University of San Carlos, Philippines
Volume: 9 | Issue: 2 | Pages: 3965-3970 | April 2019 | https://doi.org/10.48084/etasr.2664

Abstract

Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.

Keywords:

spectral, reflectance, radiance, water quality modelling, geoinformatics

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References

F. Mushtaq, A. C. Pandey, “Assessment of land use/land cover dynamics vis-a-vis hydrometeorological variability in Wular Lake environs Kashmir Valley, India using multitemporal satellite data”, Arabian Journal of Geosciences, Vol. 7, No. 11, pp. 4707-4715, 2014 DOI: https://doi.org/10.1007/s12517-013-1092-1

F. Mushtaq, M. G. Nee Lala, A. C. Pandey, “Assessment of pollution level in a Himalayan Lake, Kashmir, using geomatics approach”, International Journal of Environmental Analytical Chemistry, Vol. 95, No. 11, pp. 1001-1013, 2015

M. Gholizadeh, A. Melesse, L. Reddi, “A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques”, Sensors, Vol. 16, No. 8, p. 1298, 2016 DOI: https://doi.org/10.3390/s16081298

S. J. Goetz, N. Gardiner, J. H. Viers, “Monitoring freshwater, estuarine and near-shore benthic ecosystems with multi-sensor remote sensing: An introduction to the special issue”, Remote Sensing of Environment, Vol. 112, No. 11, pp. 3993-3995, 2008 DOI: https://doi.org/10.1016/j.rse.2008.05.016

J. Kibena, I. Nhapi, W. Gumindoga, “Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River , Zimbabwe”, Physics and Chemistry of the Earth, Parts A/B/C, Vol. 67-69, pp. 153-163, 2014 DOI: https://doi.org/10.1016/j.pce.2013.09.017

E. Alparslan, C. Aydoner, V. Tufekci, H. Tufekci, “Water quality assessment at Omerli Dam using remote sensing techniques”, Environmental Monitoring and Assessment, Vol. 134, No. 1-3, p. 391, 2007 DOI: https://doi.org/10.1007/s10661-007-9658-6

D. G. Hadjimitsis, M. G. Hadjimitsis, L. Toulios, C. Clayton, “Use of space technology for assisting water quality assessment and monitoring of inland water bodies”, Physics and Chemistry of the Earth, Parts A/B/C, Vol. 35, No. 1-2, pp. 115-120, 2010 DOI: https://doi.org/10.1016/j.pce.2010.03.033

V. Markogianni, D. Kalvas, G. P. Petropoulos, E. Dimitriou, “An appraisal of the potential of Landsat 8 in estimating chlorophyll-a, ammonium concentrations and other water quality indicators”, Remote Sensors, Vol. 10, No. 7, pp. 1-22, 2018 DOI: https://doi.org/10.3390/rs10071018

Y. A. El-Amier, A. A. Elnaggar, M. A. El-Alfy, “Evaluation and mapping spatial distribution of bottom sediment heavy metal contamination in Burullus Lake, Egypt”, Egyptian Journal of Basic and Applied Sciences, Vol. 4, No. 1, pp. 55-66, 2017 DOI: https://doi.org/10.1016/j.ejbas.2016.09.005

A. S. Jasrotia, A. Majhi, S. Singh, “Water Balance Approach for Rainwater Harvesting using Remote Sensing and GIS Techniques, Jammu Himalaya, India”, Water Resources Management, Vol. 23, No. 14, pp. 3035-3055, 2009 DOI: https://doi.org/10.1007/s11269-009-9422-5

P. H. Gowda, J. L. Chavez, P. D. Colaizzi, S. R. Evett, T. A. Howell, J. A. Tolk, “ET mapping for agricultural water management: present status and challenges”, Irrigation Science, Vol. 26, No. 3, pp. 223-237, 2008 DOI: https://doi.org/10.1007/s00271-007-0088-6

I. Klein, A. J. Dietz, U. Gessner, A. Galayeva, A. Myrzakhmetov, C. Kuenzer, “Evaluation of seasonal water body extents in Central Asia over thepast 27 years derived from medium-resolution remote sensing data”, International Journal of Applied Earth Observation and Geoinformation, Vol. 26, No. 1, pp. 335-349, 2014 DOI: https://doi.org/10.1016/j.jag.2013.08.004

C. Ye, “Extraction of water body in before and after images of flood using Mahalanobis distance-based spectral analysis”, Remote Sensing, Vol. 31, No. 4, pp. 293-302, 2015 DOI: https://doi.org/10.7780/kjrs.2015.31.4.2

Y. Wang, H. Xia, J. Fu, G. Sheng, “Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data”, The Science of the Total Environment, Vol. 328, No. 1-3, pp. 195-206, 2004 DOI: https://doi.org/10.1016/j.scitotenv.2004.02.020

R. Swain, B. Sahoo, “Improving river water quality monitoring using satellite data products and a genetic algorithm processing approach”, Sustainability of Water Quality and Ecology, Vol. 9-10, pp. 88-114, 2017 DOI: https://doi.org/10.1016/j.swaqe.2017.09.001

L. C. Gonzalez-Marquez, F. M. Torres-Bejarano, A. C. Torregroza-Espinosa, I. R. Hansen-Rodriguez, H. B. Rodriguez-Gallegos, “Use of LANDSAT 8 images for depth and water quality assessment of El Guajaro reservoir, Colombia”, Journal of South American Earth Sciences, Vol. 82, pp. 231-238, 2018 DOI: https://doi.org/10.1016/j.jsames.2018.01.004

A. El-Zeiny, S. El-Kafrawy, “Assessment of water pollution induced by human activities in Burullus Lake using Landsat 8 operational land imager and GIS”, The Egyptian Journal of Remote Sensing and Space Science, Vol. 20, Suppl. 1, pp. S49-S56, 2017 DOI: https://doi.org/10.1016/j.ejrs.2016.10.002

A. M. El Saadi, M. M. Yousry, H. S. Jahin, “Statistical estimation of Rosetta branch water quality using multi-spectral data”, Water Science, Vol. 28, No. 1, pp. 18-30, 2014 DOI: https://doi.org/10.1016/j.wsj.2014.10.001

A. Kulkarni, “Water Quality Retrieval from Landsat TM Imagery”, Procedia Computer Science, Vol. 6, pp. 475-480, 2011 DOI: https://doi.org/10.1016/j.procs.2011.08.088

V. Markogianni, D. Kalivas, G. Petropoulos, E. Dimitriou, “Analysis on the feseability of L8 for WQPs assessment”, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, Vol. 11, No. 9, pp. 906-914, 2017

E. Kontopoulou, P. Kolokoussis, K. Karantzalos, “Water quality estimation in Greek lakes from Landsat 8 multispectral satellite data”, European Water, Vol. 58, pp. 191-196, 2017

M. Waxter, Analysis of Landsat Satellite Data to Monitor Water Quality Parameters in Tenmile Lake, Oregon, MSc Thesis, Portland State University, 2014

T. S. Kapalanga, Assessment and Development of Remote Sensing Based Algorithms For Water Quality Monitoring in Olushandja Dam, North-Central Namibia, MSc Thesis, University of Zimbabwe, 2015

D. Barrett, A. Frazier, “Automated Method for Monitoring Water Quality Using Landsat Imagery”, Water, Vol. 8, No. 6, p. 257, 2016 DOI: https://doi.org/10.3390/w8060257

V. Kumar, A. Sharma, A. Chawla, R. Bhardwaj, A. K. Thukral, “Water quality assessment of river Beas, India, using multivariate and remote sensing techniques”, Environmental Monitoring and Assessment, Vol. 188, No. 3, p. 137, 2016 DOI: https://doi.org/10.1007/s10661-016-5141-6

K. W. Abdelmalik, “Role of statistical remote sensing for Inland water quality parameters prediction”, The Egyptian Journal of Remote Sensing and Space Science, Vol. 21, No. 2, pp. 193-200, 2018 DOI: https://doi.org/10.1016/j.ejrs.2016.12.002

R. Swain, B. Sahoo, “Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries”, Journal of Environmental Management, Vol. 192, pp. 1-14, 2017 DOI: https://doi.org/10.1016/j.jenvman.2017.01.034

Environmental Management Bureau-Caraga Region, Tubay River Water Quality Assessment, 2015

G. Chander, B. Markham, D. Helder, “Summary of Current Radiometric Calibration Coefficients for”, Remote Sensing of Environment, Vol. 1, No. 2009, pp. 1-24, 2009 DOI: https://doi.org/10.1016/j.rse.2009.01.007

S. M. Adler-Golden, A. Berk, L. S. Bernstein, S. Richtsmeier, P. K. Acharya, M. W. Matthew, G. P. Anderson, C. L. Allred, L. S. Jeong, J. H. Chetwynd, “FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations”, in: Proceedings of the 7th Annual JPL Airborne Earth Science Workshop, Vol. 97, JPL Publication, 1998

P. S. Chavez, “An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data”, Remote Sensing of Environment, Vol. 24, No. 3, pp. 459-479, 1988 DOI: https://doi.org/10.1016/0034-4257(88)90019-3

G. Srivastava, P. Kumar, “Water quality index with missing parameters”, International Journal of Research in Engineering and Technology, Vol. 2, No. 4, pp. 609-614, 2013 DOI: https://doi.org/10.15623/ijret.2013.0204035

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How to Cite

[1]
M. V. Japitana and M. E. C. Burce, “A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 2, pp. 3965–3970, Apr. 2019.

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