JWSS - Journal of Water and Soil Science

Identification and investigation of changes in the area under cultivation of various crops seem to be essential for the management supply of crop production. In this study, r to identify and investigate change of the area under cultivation in major crop Hoseynabade Mishmast region in Qom province, we used the time series images of OLI and ETM sensors of landsat 8 and 7satellites, according to the crop calendar of this region. By using the vegetation index (NDVI) in the decision tree algorithm, the thresholds of this index were adjusted according to the major crops of this region; then a map of the cultivation pattern of the crop of this region was prepared. In order to evaluate the results, the statistics of the provinces agricultural jihad were used during 2005, 2009, 2014 and 2019 crop years. The results showed that by using the threshold of NDVI index, crops in this region in 2005 included wheat and barley and alfalfa, and their areas had an error of 17/1 and 6/1 percent in comparison with the statistics of agricultural Jihad, respectively; in 2009, wheat and barley, alfalfa and corn had an error of 0/5, 9/6 and 0/1 percent. Also, in 2014, wheat and barley, alfalfa, corn and sophie crops had an error equal to 4/9, 0.4, 11/4 and 2/4 percent, and the same crops in 2019 had an error 0/04, 11/6, 1/4 and 17/5 percent; that error was not significant. According to the results, the appropriate efficiency NDVI index in estimating crop cultivation area was determined by their phenology. Also, in 2009 and 2014, corn and sophie crops were added to the regions crops, and the area under crops cultivation in 2019 was increased, as compared to 2014.

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.