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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