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

  • Weicheng WuEmail author
Chapter

Abstract

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

ANN

Artificial neural networks

ARVI

Atmospherically resistant vegetation index

CNN

Convolutional neural networks

DEM

Digital elevation model

EVI

Enhanced vegetation index

GDVI

Generalized difference vegetation index

IBL

Instance-based learning

LST

Land surface temperature

ML

Maximum likelihood

MLR

Multivariate linear regression

NDVI

Normalized difference vegetation index

NDII

Normalized difference infrared index

NIR

Near infrared

OSAVI

Optimized soil-adjusted vegetation index

PCA

Principal components analysis

PLSR

Partial least square regression

R

Red

RF

Random forests

RFR

Random forest regression

SAR

Synthetic-aperture radar

SARVI

Soil-adjusted and atmospherically resistant vegetation index

SAVI

Soil-adjusted vegetation index

SI

Salinity index

SVM

Support vector machines

SVR

Support vector regression

SWIR

Shortwave infrared

VNIR

Visible and near infrared

Notes

Acknowledgment

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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