Abstract
In this study, empirical models were generated to estimate water quality parameters, with the objective of showing the benefits of the satellite remote sensing application in the characterization of coastal waters. The study area was Playa Colorada Bay, located in the northwest of Mexico, in the eastern part of the Gulf of California. In two seasons of the year, on-site and laboratory characterizations were carried out to determine the spatial and temporal variation of phosphates (PO4), electrical conductivity (EC), total suspended solids (TSS), turbidity, and pH of water. Samplings were selected to match Landsat 8 satellite overpass in the study area. Radiometric and atmospheric corrections were applied to the images, prior to the generation of the models. The models were generated using the linear regression technique of successive steps; water quality parameters and their logarithms were used as dependent variables, and as independent variables were used corrected reflectance values of Landsat images. The results showed that the concentration of PO4 in the analyzed water samples were higher than those recommended in the Mexican ecological criteria of water quality, to protect the aquatic life of marine water in coastal areas. In autumn, PO4 was correlated with turbidity, T, pH, and TSS. The highest correlation coefficients were presented by TSS with PO4 (r = − 0.979) and pH (r = 0.958). The water quality models that were generated had coefficients of determination (R2) in the range of 0.637 to 0.955 and show the viability of the application of Landsat 8 images in the characterization of water quality parameters in Playa Colorada Bay. Models allowed the estimation of the distribution of water quality parameters over the whole bay instead of only at the sampling stations, favoring a better understanding of their spatial distribution.
Similar content being viewed by others
References
APHA-AWWA-WEF (2012) Standard methods for the examination of water and wastewater. In: E. W. Rice, R. B. Baird, A. D. Eaton, & L. S. Clesceri (Eds.) (22nd ed.). American Public Health Association, American Water Works Association, Water Environment Federation
Berthouex PM, Brown LC (2002) Statistics for environmental engineers. Lewis Publishers, Boca Raton
Bonansea M, Rodriguez MC, Pinotti L, Ferrero S (2015) Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sens Environ 158:28–41. https://doi.org/10.1016/j.rse.2014.10.032
Brezonik P, Menken KD, Bauer M (2005) Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake Reservoir Manage 21(4):373–382. https://doi.org/10.1080/07438140509354442
Chang N-B, Imen S, Vannah B (2014) Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit Rev Environ Sci Technol 45(July 2015):140225124012009–140225124012166. https://doi.org/10.1080/10643389.2013.829981
Chavez PS (1996) Image-based atmospheric corrections—revisited and improved. Photogramm Eng Remote Sens 62(9):1025–1036
CLICOM. (2016). Datos climáticos diarios del CLICOM del SMN a través de su plataforma web del CICESE. Retrieved September 6, 2016, from http://clicom-mex.cicese.mx
Congedo L (2016) Semi-automatic classification plugin documentation. https://doi.org/10.13140/RG.2.2.29474.02242/1
Correll DL (1998) The role of phosphorus in the eutrophication of receiving waters: a review. J Environ Qual 27(2):261. https://doi.org/10.2134/jeq1998.00472425002700020004x
DOF (1989) Criterios Ecológicos de Calidad del Agua CE- CCA-001/8. Diario Oficial de la Federación. México, 13 de diciembre de 1989. http://www.dof.gob.mx/nota_detalle.php?codigo=4837548&fecha=13/12/1989
El Saadi AM, Yousry MM, Jahin HS (2014) Statistical estimation of Rosetta branch water quality using multi-spectral data. Water Sci 28(1):18–30. https://doi.org/10.1016/j.wsj.2014.10.001
Gholizadeh MH, Melesse AM, Reddi L (2016) A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Basel, Switzerland), 16(8), 1298. doi: https://doi.org/10.3390/s16081298
González-Márquez LC, Figueroa Moreno MA., Hansen Rodríguez IR, Rodríguez Gallegos HB, Trigueros Salmerón Á (2014) Fosfatos en agua de drenaje agrícola: cuenca baja del Río Sinaloa. Ciencia Desde El Occidente, 1(2), 19–28. Retrieved from http://udo.mx/portal/images/archivos/cienciadesdeeloccidente/2016/VOL1/VOL1 No2 2015 ARTÍCULO 3.pdf
González-Márquez LC, Torres-Bejarano FM, Torregroza-Espinosa AC, Hansen-Rodríguez IR, Rodríguez-Gallegos HB (2018) Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia. J S Am Earth Sci 82(March 2018):231–238. https://doi.org/10.1016/j.jsames.2018.01.004
HACH (2014) PhosVer 3 phosphate reagent powder pillows. Retrieved December 1, 2014, from http://www.hach.com/phosver-3-phosphate-reagent-powder-pillows-10-ml-pk-100/product?id=7640196043
Harvey ET, Kratzer S, Philipson P (2015) Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters. Remote Sens Environ 158:417–430. https://doi.org/10.1016/j.rse.2014.11.017
Khattab MFO, Merkel BJ (2013) Application of Landsat 5 and Landsat 7 images data for water quality mapping in Mosul Dam Lake, Northern Iraq. Arab J Geosci 7(9):3557–3573. https://doi.org/10.1007/s12517-013-1026-y
Kloiber SM, Brezonik PL, Olmanson LG, Bauer ME (2002) A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens Environ 82(1):38–47. https://doi.org/10.1016/S0034-4257(02)00022-6
Kumar V, Sharma A, Chawla A, Bhardwaj R, Thukral AK (2016) Water quality assessment of river Beas, India, using multivariate and remote sensing techniques. Environ Monit Assess,188(3):1-10. https://doi.org/10.1007/s10661-016-5141-6
Li Y, Zhang Y, Shi K, Zhu G, Zhou Y, Zhang Y, Guo Y (2017) Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery. Sci Total Environ 599–600:1705–1717. https://doi.org/10.1016/j.scitotenv.2017.05.075
Lim J, Choi M (2015) Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environ Monit Assess 187(6):1–17. https://doi.org/10.1007/s10661-015-4616-1
Lyle Fritch PL (2003) Laguna Playa Colorada-Santa María La Reforma. Ficha Informativa de los Humedales de Ramsar (FIR). Mazatlán, Sinaloa. Retrieved from https://rsis.ramsar.org/RISapp/files/RISrep/MX1340RIS.pdf
Ma R, Dai J (2005) Investigation of chlorophyll-a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Taihu Lake, China. Int J Remote Sens 26(13):2779–2795. https://doi.org/10.1080/01431160512331326648
Pavelsky TM, Smith LC (2009) Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada. Water Resources Research, 45(11), art. no.W11417. https://doi.org/10.1029/2008WR007424
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., … Zhu, Z. (2014). Remote sensing of environment Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ, 145:154–172. https://doi.org/10.1016/j.rse.2014.02.001
Sosa-Avalos R, Gaxiola-castro G, Olivos-Ortiz A, Silva-Iñiguez L (2013) Nutrientes inorgánicos y producción del fitoplancton en una laguna costera subtropical de México. Rev Biol Mar Oceanogr 48(1):143–154. https://doi.org/10.4067/S0718-19572013000100012
Theologou I, Patelaki M, Karantzalos K (2015) Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data? ISPRS-Int Arch Photogramm Remote Sens Spat Inf Sci XL-7-W3(1):1511–1516. https://doi.org/10.5194/isprsarchives-XL-7-W3-1511-2015
Uusitalo R, Yli-Halla M, Turtola E (2000) Suspended soil as a source of potentially bioavailable phosphorus in surface runoff waters from clay soils. Water Res 34(9):2477–2482. https://doi.org/10.1016/S0043-1354(99)00419-4
Wu G, de Leeuw J, Skidmore AK, Liu Y, Prins HHT (2009) Performance of Landsat TM in ship detection in turbid waters. Int J Appl Earth Obs Geoinf 11(1):54–61. https://doi.org/10.1016/j.jag.2008.07.001
Wu C, Wu J, Qi J, Zhang L, Huang H, Lou L, Chen Y (2010) Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data. Int J Remote Sens 31(9):2309–2324. https://doi.org/10.1080/01431160902973873
Yacobi YZ, Moses WJ, Kaganovsky S, Sulimani B, Leavitt BC, Gitelson AA (2011) NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study. Water Res 45(7):2428–2436. https://doi.org/10.1016/j.watres.2011.02.002
Yunus AP, Dou J, Sravanthi N (2015) Remote sensing of chlorophyll-a as a measure of red tide in Tokyo Bay using hotspot analysis. Remote Sens Appl: Soc Environ 2:11–25. https://doi.org/10.1016/j.rsase.2015.09.002
Zhang, Y., Zhang, Y., Zha, Y., Shi, K., Zhou, Y., & Wang, M. (2015). [Remote sensing estimation of total suspended matter concentration in Xin’anjiang Reservoir using Landsat 8 data]. Huan Jing Ke Xue= Huanjing Kexue, 36(1), 56–63. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25898647
Zheng Z, Li Y, Guo Y, Xu Y, Liu G, Du C (2015) Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China. Remote Sens 7(10):13975–13999. https://doi.org/10.3390/rs71013975
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
González-Márquez, L.C., Torres-Bejarano, F.M., Rodríguez-Cuevas, C. et al. Estimation of water quality parameters using Landsat 8 images: application to Playa Colorada Bay, Sinaloa, Mexico. Appl Geomat 10, 147–158 (2018). https://doi.org/10.1007/s12518-018-0211-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12518-018-0211-9