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

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.

Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran.

In recent years, indirect methods have been used to estimate soil salinity in agricultural lands. In this research, the electrical conductivity of 93 soil samples from 0 to 30 cm and 0 to 100 cm was measured using the hypercube technique at Sharifabad-Saveh Plain, Iran. Land area parameters such as TWI, TCI, STP, DEM, and LS were used as topographic variables and spatial indices of salinity and vegetation were derived from Landsat 8 images. Soil salinity off crops and gardens was determined at 0-30 cm and 0-100 cm. The data were divided into two series: the training set (70%) and the test set (30%). In order to model and predict salinity, models such as an artificial neural network (ANN), integration of neural network and genetic algorithm (ANN-GA), PLSR, and decision tree (DT) were used. The results of the models' evaluation based on MSE and R2 indices showed that the ANN-GA model has the highest accuracy in predicting soil properties. This model improved the accuracy of soil salinity prediction by 28%, 42%, and 23% in 0-30 cm and by 20%, 28%, and 25% at 100 cm than ANN, PLSR, and DT. The result showed the 2 dS/m EC at alfalfa and cucurbits farmlands while pistachio orchards have low salinity and bare lands have moderate and high salinity.