A Brief Review on Soil Salinity Mapping by Optical and Radar Remote Sensing

  • Weicheng WuEmail author


This paper summarized the recent progress in soil salinity detection, prediction, quantification, and mapping by remote sensing technology. The following aspects such as classification-based mapping technique, biophysical indicators and spectral indices application, potential of radar data, and machine learning regression were all reviewed.

Keywords and Abbreviations


Artificial neural networks


Atmospherically resistant vegetation index


Convolutional neural networks


Digital elevation model


Enhanced vegetation index


Generalized difference vegetation index


Instance-based learning


Land surface temperature


Maximum likelihood


Multivariate linear regression


Normalized difference vegetation index


Normalized difference infrared index


Near infrared


Optimized soil-adjusted vegetation index


Principal components analysis


Partial least square regression




Random forests


Random forest regression


Synthetic-aperture radar


Soil-adjusted and atmospherically resistant vegetation index


Soil-adjusted vegetation index


Salinity index


Support vector machines


Support vector regression


Shortwave infrared


Visible and near infrared



The author would like to thank East China University of Technology for their financial support (Grant No: DHTP2018001, 2018-2022).


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Key Laboratory of Digital Land & ResourcesEast China University of TechnologyNanchangChina

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