عنوان مجله
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INTERNATIONAL JOURNAL OF REMOTE SENSING
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چکیده
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In this article, the possibility of predicting soil electrical conductivity
(EC) from the enhanced thematic mapper plus (ETM+) data is
studied using the multiple regression technique. In this regard, soil
EC was measured in 188 points in an area of 5000 ha, Western
Urmia Lake, northwest of Iran. ETM+ data for sampling dates were
also acquired. Then, the measured EC (as the dependent variable)
and reflectance from the original bands of ETM+ data and their
ratios besides the different extracted indices (as independent
variables) were used to construct different regression relations to
predict soil salinity. Different data reduction algorithms, including
principal component (PC) analysis, minimum noise fraction (MNF)
transformation, pixel purity index (PPI), and n-dimensional visualizer
(nDV) algorithms, were also applied to extract the independent
factors of original bands. During the construction of regression
relations, different criteria including normalized difference vegetation
index (NDVI) and RNIR=RSWIR1(R is reflectance and NIR and
SWIR1 are near-infrared and shortwave infrared 1 bands of ETM+
data, respectively) were applied to group data sets in order to
increase the prediction accuracies. Results revealed that the constructed
regression relations were robust enough to predict the
soil salinity showing adjusted determination coefficient (R2) up to
0.875. The best equation was obtained for the data set with NDVI
values higher than 0.35. In general, the results show that the
multiple regression technique, along with remotely sensed data,
has enough accuracy to predict soil salinity.
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