Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data

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 (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.