JWSS - Journal of Water and Soil Science

Determination of land suitability is one of the land evaluation methods that can determine the best use of land in each area. The purpose of this research was to determine the land suitability of Mirza Kuchak Khan's cultivation and industry fields based on the soil quality indicators and a geographic information system (GIS), and compare the results with those obtained by methods of land evaluation and root strategies. For this purpose, information on soil profiles and the amount of organic matter, phosphorus, nitrogen, potassium, zinc, drainage, texture, depth, topography, surface rocks and gravel, impervious layer depth, hydraulic conductivity, water holding capacity, electrical conductivity, reaction PH), calcium carbonate, and exchangeable sodium percent of the study area were collected. Land suitability classes based on the quality indices of fertility, chemical quality, and physical quality of soil were defined. The results showed that 27.4% of the land belonged to the very good class (S1), 62.83% of the land could be assigned to the suitable class (S2), 11.7% of the land was put in the low proportion class (S3), and 2.66% the land was in the inappropriate class (N). Also, based on the comparison of the results of the method based on the soil quality with the square root method, Kappa coefficient was 0.82, while it was equal to 0.38 for the Storie method.

Soil salinity and its development are the main problems that should be prevented by correct management methods. Recognition of saline districts and the preparation of salinity maps are the first steps in this way. Nowadays, the application of auxiliary data in digital soil mapping is increasing due to the current associated problems in the preparation of traditional maps. The objectives of this study were to map soil salinity by the Regression Kriging (RK) method,  to identify areas with high salinity, and to investigate the relationship between soil salinity and soil-forming factors in Khuzestan Province. For this purpose, 291 surface soil samples (0-10 cm) were randomly collected in April 2014. Auxiliary variables or soil-forming factors were included in the land parameters such as slope, watershed and wetness index, OLI and TIRS images of Landsat 8, and the category maps (soil, land use, and geological maps). Also, kriging approaches were used to compare the precision of different mapping methods. The results indicated that the Regression Kriging method has a higher precision compared with other methods so that the coefficient of determination, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were estimated as 0.84, 0.41, and 6.21, respectively. The Decision Tree Regression method could also create a good relationship between soil salinity and auxiliary variables. The results showed that some auxiliary variables were more effective on the prediction of soil salinity including 2, 4, 5, and 7 bands of Landsat 8, Brightness Index, Wetness Index, Multiresolution index of Valley Bottom Flatness (MrVBF), Channel Network Base Level (CNBL), NDVI, SAVI and soil map. A Digital map of soil salinity was prepared by the obtained rules, and then it was assimilated with the map of error of variance to prepare the final soil salinity map. Accordingly, soil salinity was found to have an increasing trend from north to south in Khuzestan Province which indicates a salinity problem in the south of the Province. The main reasons for the high salinity in the south and southwestern parts of the area could be attributed to the high water table levels, differences in topography, capillary movement of salt to the soil surface, the difference in the type of land uses, and also groundwater quality and irrigation water which is altered by the frequent application of wastewaters and animal manures.