Digital Mapping of Soil Salinity Using Auxiliary Data and Machine Learning Models in Badr Watershed, Kurdistan Province

Use of remote sensing and machine learning techniques are increasingly recognized as cost-effective methods for displaying soil salinity maps. In this study, Landsat 8 satellite data and sophisticated machine learning techniques were used to map and evaluate soil salinity levels in the Badr Watershed. In this study, several Machine Learning techniques were used to predict salinity values in Badr Watershed. These algorithms included K-nearest neighbor (KNN), decision tree analysis (DTA), artificial neural network (ANN), random forest (RF) and mixed multivariate linear regression (MLR). In the first stage, auxiliary data such as Landsat 8 satellite images of the region and a digital elevation model with a spatial resolution of 10 meters were prepared from the country's Mapping Organization. The geological map of Qorveh was prepared from the geological site of the country, and the geological map of the Badr Watershed was extracted from it and digitized in the environment of the geographic information system. The geomorphological map was drawn and the location of the observation points was determined. Then, modeling was done, digital maps of soil classes and characteristics were prepared and the models were evaluated. Based on the Latin Supercube Technique, 125 outcrops were selected and excavated in the study area. After air-drying in the laboratory, the soil samples were pounded and passed through a 2 mm sieve. Then, soil salinity was measured. In order to estimate soil characteristics, two different conditions were investigated in this study. In the first case, ANN models, DTA and linear MLR were used for prediction. Also, to combine the results of the models, the nearest KNN was used. The results showed that the important auxiliary variables in predicting soil salinity, in order of importance, were geomorphology, depth of the valley, smoothness index of the ridge with a high degree of resolution, wetness index, slope direction, digital height model, basin slope, relative position of the slope, slope amount and slope length. Also, the results of the evaluation showed that among the models used to predict salinity, the combined MLR model with a coefficient of determination of 0.611 and a square root mean square error of 0.032 had the highest accuracy for prediction.