Volume 25, Issue 3 (Fall 2021)                   jwss 2021, 25(3): 159-175 | Back to browse issues page

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Savari Z, Hojati S, Taghizadeh Mehrjerdi R. Digital Mapping of Surface Soil Salinity in Khuzestan Province, Using Regression Kriging. jwss 2021; 25 (3) :159-175
URL: http://jstnar.iut.ac.ir/article-1-4083-en.html
Shahid Chamran University of Ahvaz , s.hojati@scu.ac.ir
Abstract:   (1441 Views)
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.
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Type of Study: Research | Subject: Ggeneral
Received: 2020/10/5 | Accepted: 2021/01/13 | Published: 2021/12/1

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