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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 401–405, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-401-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 401–405, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-401-2020

  21 Aug 2020

21 Aug 2020

SPATIO-TEMPORAL SALINITY MONITORING OF THE GHAGHARA RIVER USING LANDSAT TIME-SERIES IMAGERY AND MULTIPLE REGRESSION ANALYSIS

M. Gašparović1 and S. K. Singh2 M. Gašparović and S. K. Singh
  • 1Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kačićeva 26, Zagreb, Croatia
  • 2K. Banerjee Centre of Atmospheric & Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad-211002, Uttar Pradesh, India

Keywords: Spatio-temporal, Monitoring, Water salinity, Modelling, Landsat

Abstract. Nowadays, water has become one of the most important environmental issues for our ecosystem and is facing major challenges today. During the COVID-19 pandemic, the world has understood the need for good quality of water for sanitation and hygiene. Earth observing satellites plays a critical role in near-real-time detection and monitoring of land and water change and quality. This research presents a methodology for modeling and mapping water salinity in high spatial resolution. Data for modeling were measured on the five monitoring stations (Ayodhya, Basti, Birdghat, Paliakalan, and Turtipar) along the Ghagraha River Basin in India, during the period of 28 years (1985–2013). In this research, Electrical Conductivity (EC) as water salinity parameter modeled by means of Landsat 5 satellite imagery. All available Landsat 5 imagery were acquired on the same date as the ground measurement data was utilized for the modeling. Modeling was done based on linear, 2nd and 3rd polynomial multiple regression analysis. All statistical parameters for accuracy assessment show that 3rd degree polynomial performs better EC prediction capability than 2nd degree polynomial and linear regression. The 3rd degree polynomial multiple regression model RMSE, R2, MAE, p-value were 8.682, 0.993, 6.493, 0.008, respectively. The developed algorithm provides new knowledge that can be widely applied in various environmental research mapping and monitoring like water salinity. Also, this method allows rapid detection of water pollution, which has an important impact on human health, agriculture, and the environment.