Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model - Advances in Remote Sensing

ARS > Vol.8 No.3, September 2019

Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model ()

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DOI: 10.4236/ars.2019.83005 938 Downloads 2,941 Views Citations

Author(s)

Sarra Hihi¹, Zouhair Ben Rabah², Moncef Bouaziz^1,3, Mahmoud Yassine Chtourou¹, Samir Bouaziz¹

Affiliation(s)

¹3E Laboratory, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia.
²Centre National de Cartographie et de Télédétection, Tunis, Tunisia.
³Faculty of Environmental Sciences, Institute of Geographie, Dresden, Germany.

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.

KEYWORDS

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

Share and Cite:

Hihi, S. , Rabah, Z. , Bouaziz, M. , Chtourou, M. and Bouaziz, S. (2019) Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model. Advances in Remote Sensing, 8, 77-88. doi: 10.4236/ars.2019.83005.

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