Digital Mapping of Topsoil Salinity Using Remote Sensing Indices in Agh-Ghala Plain, Iran

Background: Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences. Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of ۱۵۶ topsoil samples (depth of ۰-۱۵cm) was also determined. A Pearson correlation between ۲۶ spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int۱, Int۲), soil salinity indices (Si۱, Si۲, Si۳, Si۱۱, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models. Results: Comparison between two estimation models showed that the performance of ANN model (R۲=۰.۹۶۴ and RMSE=۲.۲۳۷) was more reliable than that of MLR model (R۲=۰.۵۰۶ and RMSE=۹.۶۷۴) in monitoring and predicting soil salinity. Out of the total area, ۶۶% and ۵۵.۸% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively. Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity.

کلیدواژه ها:

Artificial Neural Network ، Electrical conductivity ، Landsat OLI data ، Multiple linear regression ، Iran