Mapping soil salinity using a combined spectral and topographical indices with artificial neural network.

Abstract

Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 and Artificial Neural Network (ANN) to monitor soil salinity in Qom plain. The geographical location of 72 surface soil samples from 7 land types was determined by the Latin hypercube method, and the samples were taken to determine the electrical conductivity (EC). Thirty percent of the data was considered as a validation set and 70% as a test set. In addition to the Landsat 8 bands, we used spectral indices of salinity, vegetation, topography, and drainage (DEM, TWI, and TCI) because of their impacts on soil formation and development. We used ANN with different algorithms to model soil salinity. We found that the GFF algorithm is the best for soil salinity modeling. Also, the TWI topography index and SI5 salinity index and NDVI vegetation index had the most effect on the outputs of the selected model. It was also found that flood plains and lowlands had the highest levels of salinity accumulation.