Soil salinity mapping by remote sensing south of Urmia Lake, Iran
Introduction
Soil salinity leads to land degradation, the decline in soil quality and lack of fertility in arid and semi-arid regions (Sidike et al., 2014; Makinde and Oyelade, 2019). Land degradation caused by soil salinity has been a global issue in dry regions especially in the Middle East (Qadir et al., 2006). Same as the other part of the arid environments, Lakes are exposed to the risk of severe degradation.
In this research the changes in soil salinity of the land surrounding Urmia Lake in Iran, which is the greatest salty Lake in the Middle East and the sixth-largest saltwater Lake on Earth, has been detailed studied. UNESCO Biosphere Reserve in 1976 and UNESCO's Man and the Biosphere (MAB) Program (UNESCO 2009) registered it as a protected region. Recently, the lake has been highly endangered and reduced by about 88% (AghaKouchak et al., 2015). Along with receding of the shoreline, salt accumulation (up to more than 300 g/l) is another prevalent problem (Ahmadzadeh Kokya et al., 2011). If the lake is dry, the region's temperate climate will become tropical and windblown salts will lead to agricultural land degradation, water resources poisoning and respiratory problems for millions of human beings.
Recently, remote sensing techniques have been widely utilized for monitoring studies of salinity due to the high reflectance of saline soil in the visible and infrared range (Farifteh et al., 2007; Alavipanah and Nezammahalleh, 2013, Yu et al., 2018). Updating maps using this technique is quicker and more straightforward than in the conventional methods (Farifteh et al., 2007; Bierwirth and Brodie, 2008; Mulder et al., 2011; Taghizadeh-Mehrjardi et al., 2014).
Remote sensing techniques using intelligent algorithms can predict surface salinity at various time intervals in large-scale regions (Metternicht and Zinck, 2003; Bouaziz et al., 2011; Sidike et al., 2014). Among the intelligent algorithms, the neural networks-genetic algorithms hybrid model (ANN-GA) has mainly been utilized to study the nonlinear relationship between earthly measurements and satellite data with higher accuracy compared to linear models (Seyam and Mogheir, 2011; Phonphan et al., 2014; Naderi et al., 2017; Ghimire et al., 2019; Li and Wang, 2019; Wang et al., 2018). The evolutionary algorithms like GA are coupled with ANN for optimizations of model parameters. Giving more details, the GA is applied to adjust the weights of the ANN properly and obtain the proper network topology (Icaga, 2005; Chang et al., 2010).
The present study first tries to use predict salinity distribution around Lake Urmia using satellite imagery and the ANN-GA hybrid model in 2018 with the real soil samples. Secondly, this study explores changes in the soil surface salinity in the study area from 1985 to 2018 through the processing of satellite images with applying the ANN-GA algorithm without soil samples data. Lastly, the soil salinity map of 1973 as the oldest salinity map of the study area, digitized and utilized in the evaluation of salinity changes. To the best of the authors' knowledge, this study is the first one of this kind conducted in this region.
Section snippets
Location of the studied region
Lake Urmia is a giant shallow hyper saline Lake with many islands in it, enclosed by dense slightly salty to saline marshes, mainly at its southern part. As a brief soil classification, fine textured alluvial soils, saline alluvial soils (Solonchak and Solonetz soil) and salt marsh soils are the dominant classes in the region. Drainage conditions vary from well-drained lands to waterlogged pasturelands or otherwise saline soils (halomorphic) in wastelands (Gholampour et al., 2015).
Urmia Lake
Soil analysis
The results of descriptive statistics for the soil chemical properties are presented in Table 2. The average ECe and pH of the samples are more than those for normal soil in which EC ≤ 2 and pH ≤ 7. Based on the maximum and minimum of ECe values it is evident that the salinity distribution remarkably varies from non-saline to extremely-saline soils. The average sodium absorption ratio (SAR) and exchangeable sodium percentage (ESP) illustrate the presence of sodium ions as a major cation in clay
Conclusion
In the present study, soil surface salinity values were estimated using the ANN-GA model based on the data of Landsat 7 ETM+ Satellite. The model obtained efficiency was evaluated for both supervised and unsupervised classifications. The main finding are listed below:
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The ANN-GA model was calibrated (train and test) and had considerable efficiency in salinity estimation with real samples (supervised classification).
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The distribution of the soil salinity surveyed using the calibrated model.
Declaration of Competing Interest
None.
Acknowledgment
The authors gratefully acknowledge the support of the University of Zanjan (ZNU). The authors are also grateful to Mr. Behzad Amiri and Mr. Porya Shahsavari for their aids in the soil sampling step.
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