Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 10 233 DOI: 10.4018/978-1-5225-7784-3.ch010 ABSTRACT A major part of Indo-Gangetic plain is afected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt afected soils in Indo- Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of diferentiating salt afected soils from other features were identifed as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-afected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found afected with salinity/alkalinity. The spatial distribution of salt-afected soils in the district showed an association of shallow ground water depth with salinity. Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing Nirmal Kumar National Bureau of Soil Survey and Land Use Planning, India S. K. Singh National Bureau of Soil Survey and Land Use Planning, India G. P. Obi Reddy National Bureau of Soil Survey and Land Use Planning, India R. K. Naitam National Bureau of Soil Survey and Land Use Planning, India