The effect of soil salinity on plant growth is due to two factors, namely, increase in osmotic pressure of soil solution and the ionic composition of salt. The present experiment was conducted to obtain information about the response of pistachio (Pistacia vera L.) to salinity and ionic composition of the salt. Salinity with different relative composition of sodium chloride and sodium sulfate were applied to two pistachio cultivars, Fandoghi and Badami. Yield (dry matter of leaf and shoots produced in each pot) of plants were compared by the analysis of variances method of F and Duncan tests.

The yields of the cultivars were significantly different (P≤0.01) with Fandoghi cultivar producing less shoots and leaf and, consequently, lower total dry matter. Increasing the salinity level decreased the plant growth. Leaves were more sensitive to salinity. Increasing the ratio of sulfate salt alleviated the depressive effect of salinity such that when salinity was 100% sodium sulfate, the dry matter yield of shoots was 1.5 times and that of leaf was 1.7 times higher compared to the treatment where salinity was 100% sodium chloride. Leaf was more sensitive than shoots and, therefore, it showed a more positive response to chloride decreasing.

This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.

Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km², in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.

The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region.