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Mtimet, A. (2003) Contributionàl’ étudepédologique des limons des Matmata (Sudtunisien). Doctorat de spécailité, Universitémarie & Curie, Paris.

has been cited by the following article:

  • TITLE: Prediction of Soil Salinity Using Multivariate Statistical Techniques and Remote Sensing Tools

    AUTHORS: Moncef Bouaziz, Mahmoud Yassine Chtourou, Ibtissem Triki, Sascha Mezner, Samir Bouaziz

    KEYWORDS: Remote Sensing, Spectral Indices, Soil Salinity, Principal Component Analysis, Cluster Analysis

    JOURNAL NAME: Advances in Remote Sensing, Vol.7 No.4, December 26, 2018

    ABSTRACT: Soil salinity limits plant growth, reduces crop productivity and degrades soil. Multispectral data from Landsat TM are used to study saline soils in southern Tunisia. This study will explore the potential multivariate statistical analysis, such as principal component analysis (PCA) and cluster analysis to identify the most correlated spectral indices and rapidly predict salt affected soils. Sixty six soil samples were collected for ground truth data in the investigated region. A high correlation was found between electrical conductivity and the spectral indices from near infrared and short-wave infrared spectrum. Different spectral indices were used from spectral bands of Landsat data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Landsat original bands showed that the near and short-wave infrared bands (band 4, band 5 and 7) and the salinity indices (SI 5 and SI 9) have the highest correlation with EC. The use of CA revealed a strong correlation between electrical conductivity EC and spectral indices such abs4, abs5, abs7 and si5. The principal components analysis is conducted by incorporating the reflectance bands and spectral salinity indices from the remote sensing data. The first principal component has large positive associations with bands from the visible domain and salinity indices derived from these bands, while second principal component is strongly correlated with spectral indices from NIR and SWIR. Overall, it was found that the electrical conductivity EC is highly correlated (R2 = -0.72) to the second principal component (PC2), but no correlation is observed between EC and the first principal component (PC1). This suggests that the second component can be used as an explanatory variable for predicting EC. Based on these results and combining the spectral indices (PC2 and abs B4) into a regression analysis, model yielded a relatively high coefficient of determination R2 = 0.62 and a low RMSE = 1.86 dS/m.