Soil salinity mapping by remote sensing south of Urmia Lake, Iran

• Published in: Geoderma Regional Vol. 22; p. e00317
• Main Authors: Delavar, Mohammad Amir, Naderi, Arman, Ghorbani, Yousef, Mehrpouyan, Ahmad, Bakhshi, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2020
• Subjects: algorithms; halophytes; Iran; lakes; Middle East; Mineral Processing; Mineralteknik; normalized difference vegetation index; Puffy soil; remote sensing; roughness; salinity; Salinity ratio index; satellites; sodium; soil salinity; Solonchaks; Supervised classification; time series analysis; Middle East; Iran; Salinity ratio index; Solonchaks; Puffy soil; Supervised classification
• DOI: 10.1016/j.geodrs.2020.e00317
• ISSN: 2352-0094

Urmia Lake is a shallow terminal Lake located in northwest Iran and it is one of the largest permanent Lakes in the Middle East. In this study, the changes in soil salinity at Urmia Lake were investigated using satellite images and the oldest salinity map of the area over a period of 45 years from 1973 to 2018. The distribution of salinity in 2018 was estimated using the supervised classification by the nonlinear hybrid model of artificial multi-layered neural network-genetic algorithm model (ANN-GA) while the salinity map for the years of 1985, 1995, 2005 and 2015 was estimated by the unsupervised method. Further, the salinity data of surface soil in the region for the year 1973 was also digitized and utilized. For this purpose, 291 surface samples (258 samples for modeling and 33 samples for the re-evaluation of the model) of the studied region were collected and analyzed in 2018. The input neurons were selected by analyzing the satellite imagery bands, salinity indices, salinity ratio index and normalized difference vegetation index. The correlation coefficient and root-mean-square error of the training network model were equal to 0.94 and 0.04, respectively. The salinity map of the studied region was estimated using this model and classified into six classes (S0 to S5). The produced map of 2018 was used to re-evaluate the results. It showed that lower estimation accuracy was in classes S1 and S2. The obtained results in this study indicated that roughness, moisture, the density of halophyte plants and sodium slickspot were some of the sources for estimation of errors in lower salinity classes. The time-series changes in the salinity class of estimated maps showed that S3, S4 and S5 classes have expanded between 1973 and 2018. These are in agreement with the field observation and with the other scientific reports about the studied area. •Urmia Lake is one of the largest permanent Lakes in the Middle East.•Remote sensing techniques are widely utilized for monitoring of salinity.•Neural network has high accuracy for predicting the salinity.