%0 Journal Article
%T Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model
%A Sarra Hihi
%A Zouhair Ben Rabah
%A Moncef Bouaziz
%A Mahmoud Yassine Chtourou
%A Samir Bouaziz
%J Advances in Remote Sensing
%P 77-88
%@ 2169-2688
%D 2019
%I Scientific Research Publishing
%R 10.4236/ars.2019.83005
%X <div style=\"text-align:justify;\">
	<span style=\"font-size:10pt;font-family:&quot;\">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 a</span><span style=\"font-size:10pt;font-family:&quot;\">n</span><span style=\"font-size:10pt;font-family:&quot;\"> RMSE</span><span style=\"font-size:10pt;font-family:&quot;\"> </span><span style=\"font-size:10pt;font-family:&quot;\">= 4.8</span><span style=\"font-size:10pt;font-family:&quot;\"> </span><span style=\"font-size:10pt;font-family:&quot;\">dS/m.</span>
</div>
%K Remote Sensing
%K Spectral Indices
%K Soil Salinity
%K Electrical Conductivity
%K Salinity Index
%K Regression Analysis
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=95318