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

Soil salinity refers to the amount of salts in the soil and it can be estimated by measuring the electrical conductivity (EC) of an extracted soil solution. It is considered an important component of ecosystem degradation in the world’s dry lands and can lead to desertification and other form of land degradation, such as salinization.  

Some extensive research efforts have been made by international scholars to monitor and predict saline soils using remote sensing and statistical analysis methods. In this study, the authors would 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. Therefore, the generated regression model is considered as an efficient and rapid tool to predict soil salinity over arid region, such as southern Tunisia.

Article by Moncef Bouaziz, et al, from Tunisia and Germany.

Full access: http://t.cn/EbyUA2f
Image by DanoAberdeen, from Flickr-cc.

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