Lesch, S.M., Strauss, D.J. and Rhoades, J.D. (1995) Spatial Prediction of Soil Salinity Using Electromagnetic Induction Techniques: 1. Statistical Prediction Models: A Comparison of Multiple Linear Regression and Cokriging. Water Resources Research, 31, 373-386.
https://doi.org/10.1029/94WR02179

has been cited by the following article:

• TITLE: Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model

  AUTHORS: Sarra Hihi, Zouhair Ben Rabah, Moncef Bouaziz, Mahmoud Yassine Chtourou, Samir Bouaziz

  KEYWORDS: Remote Sensing, Spectral Indices, Soil Salinity, Electrical Conductivity, Salinity Index, Regression Analysis

  JOURNAL NAME: Advances in Remote Sensing, Vol.8 No.3, September 25, 2019

  ABSTRACT: Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. Multispectral data Sentinel_2 are used to study saline soils in southern Tunisia. 34 soil samples were collected for ground truth data in the investigated region. A moderate correlation was found between electrical conductivity and the spectral indices from SWIR. Different spectral indices were used from original bands of Sentinel_2 data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Sentinel_2 original bands showed that SWIR bands (b11 and b12) and the salinity index SI have the highest correlation with EC. Based on these results and combining these remotely sensed variables into a regression analysis model yielded a coefficient of determination R2 = 0.48 and an RMSE = 4.8 dS/m.