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

作     者：Sarra Hihi Zouhair Ben Rabah Moncef Bouaziz Mahmoud Yassine Chtourou Samir Bouaziz 

作者机构：3E Laboratory National Engineering School of Sfax University of Sfax Sfax Tunisia Centre National de Cartographie et de Télédétection Tunis Tunisia Faculty of Environmental Sciences Institute of Geographie Dresden Germany 

出 版 物：《Advances in Remote Sensing》 (遥感技术进展（英文）)

年 卷 期：2019年第8卷第3期

页      面：77-88页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Remote Sensing Spectral Indices Soil Salinity Electrical Conductivity Salinity Index Regression Analysis 

摘      要：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.