Masoud, A.A., Koike, K., Atwia, M.G., El-Horiny, M. and Gemail, K.S. (2019) Mapping Soil Salinity Using Spectral Mixture Analysis of Landsat 8 OLI Images to Identify Factors Influencing Salinization in an Arid Region. International Journal of Applied Earth Observation and Geoinformation, 83, Article ID: 101944.
https://doi.org/10.1016/j.jag.2019.101944

has been cited by the following article:

• TITLE: Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements

  AUTHORS: Moncef Bouaziz, Sarra Hihi, Mahmoud Yassine Chtourou, Babatunde Osunmadewa

  KEYWORDS: Hyperion, Linear Spectral Unmixing (LSU), Spectral Indices, Ground-Truth, Soil Salinity, Gabes

  JOURNAL NAME: Journal of Geographic Information System, Vol.12 No.4, August 31, 2020

  ABSTRACT: Soil salinity is one of the serious environmental problems ravaging the soils of arid and semi-arid region, thereby affecting crop productivity, livestock, increase level of poverty and land degradation. Hyperspectral remote sensing is one of the important techniques to monitor, analyze and estimate the extent and severity of soil salt at regional to local scale. In this study we develop a model for the detection of salt-affected soils in arid and semi-arid regions and in our case it’s Ghannouch, Gabes. We used fourteen spectral indices and six spectral bands extracted from the Hyperion data. Linear Spectral Unmixing technique (LSU) was used in this study to improve the correlation between electrical conductivity and spectral indices and then improve the prediction of soil salinity as well as the reliability of the model. To build the model a multiple linear regression analysis was applied using the best correlated indices. The standard error of the estimate is about 1.57 mS/cm. The results of this study show that hyperion data is accurate and suitable for differentiating between categories of salt affected soils. The generated model can be used for management strategies in the future.