scholarly journals Investigation of soil salinity to distinguish boundary line between saline and agricultural lands in Bonab Plain, southeast Urmia Lake, Iran

2017 ◽  
Vol 20 (4) ◽  
pp. 1037
Author(s):  
Nikou Hamzehpour ◽  
Mehdi Rahmati
Keyword(s):  
Soil Salinity ◽  
Urmia Lake ◽  
Boundary Line ◽  
Agricultural Lands

scholarly journals Top soil salinity prediction in South-Western part of Urmia Lake with ground water data

2014 ◽  
Vol 4 (1) ◽  
pp. 57-63 ◽  
Author(s):  
N Hamzehpour ◽  
MK Eghbal ◽  
P Bogaert ◽  
N Toomanian
Keyword(s):  
Water Quality ◽  
Soil Salinity ◽  
Activity Ratio ◽  
Underground Water ◽  
Soil Samples ◽  
Urmia Lake ◽  
Quality Data ◽  
Agricultural Lands ◽  
Sodium Activity ◽  
Top Soil

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area. DOI: http://dx.doi.org/10.3329/ijarit.v4i1.21093 Int. J. Agril. Res. Innov. & Tech. 4 (1): 57-63, June, 2014

scholarly journals Analysis of decadal land cover changes and salinization in Urmia Lake Basin using remote sensing techniques

2017 ◽  
Author(s):  
Yusuf Alizade Govarchin Ghale ◽  
Metin Baykara ◽  
Alper Unal
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Spectral Response ◽  
Dust Storms ◽  
Urmia Lake ◽  
Lake Basin ◽  
North West ◽  
Irrigated Lands ◽  
Urmia Lake Basin ◽  
Salty Soil

Abstract. Urmia Lake located in the north-west of Iran, is one of the largest hyper-saline lakes in the world. In recent years, most of the Urmia Lake have been rendered to unusable lands. Drought and rapid increase in agricultural activities are the most important reasons behind the shrinkage of the Lake. This kind of exploitation with the added salinity from irrigation occurring over time has caused increased soil salinity in the basin leading up to desertification. Soil salinity research are crucial to understand underlying causes and consequences of the drying Urmia Lake. In this study, we use remote sensing technology and image processing techniques to detect spatio-temporal variability of salt body, salt affected lands, and development of irrigated lands to estimate the extend of salinization in terms of spectral response of satellite images for the Urmia Lake Basin from 1975 to 2016. The results of this study indicate that salt and salty soil areas has increased dramatically from 1995 to 2014 and more than 5000 km2 of Urmia Lake's water surface area was converted to salt or salty soil bodies during recent years. Salinization and desertification progress are not limited to just dried bottom of the Urmia Lake. Although the area of irrigated lands has increased more than two times during the studied period, soil salinity has increased in regions close to Urmia Lake too. This desertification in the basin have potential to be the source of dust storms, which have adverse effects on people's life and climate as well.

scholarly journals Prospects of biodrainage to mitigate problems of waterlogging and soil salinity in context of India - A review

2020 ◽  
Vol 12 (2) ◽  
pp. 229-243
Author(s):  
P. Masilamani ◽  
K. Arulmozhiselvan ◽  
A. Alagesan
Keyword(s):  
Soil Salinity ◽  
Water Productivity ◽  
Woody Species ◽  
Command Area ◽  
Agricultural Lands ◽  
Active Growth ◽  
Long Run ◽  
Canal Command Area ◽  
High Skill ◽  
Tolerance To Salinity

Major parts of agricultural lands in arid and semi-arid regions of India are affected by soil salinity and waterlogging in canal command area and outside. Waterlogging is caused by a rising water table and poor drainage conditions.  Stress due to waterlogging and salinity are serious to plants in all stages from seed germination to active growth and maturity. Unmanaged affected agricultural lands turn into low productive marshlands in the long run. Physical provision of surface or sub-surface drainage structures can rescue in such a situation. Yet, high skill and investment are required in the installation and maintenance of such structures. Alternatively, biodrainage method has been evolved as an effective method recently world over. In biodrainage, plants are raised over a larger area, which can transpire and remove an enormous amount of water from the soil. Plants having adequate adaptive traits and tolerance mechanisms are desirable to mitigate waterlogging and salinity. Biodrainage is suitable in rainfed and irrigated conditions. Planting of right plant species in optimum population and geometry decides the efficiency of biodrainage. Further, combining biodrainage with the conventional drainage can improve land and water productivity. Eucalyptus is the most suitable tree species for biodrainage as it has well performed in versatile environments. It possesses appreciable tolerance to salinity, sodicity and waterlogged conditions of the soil.  Fast-growing with a straight trunk, deep rooting ability, low shading effect and high transpiration capacity are promising characteristics of this tree.  Prominent woody species like Acacia nilotica, Dalbergia sissoo, Hardwickia binata can also be grown for high profit.

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

2020 ◽  
Vol 22 ◽  
pp. e00317
Author(s):  
Mohammad Amir Delavar ◽  
Arman Naderi ◽  
Yousef Ghorbani ◽  
Ahmad Mehrpouyan ◽  
Ali Bakhshi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Urmia Lake

scholarly journals Remote sensing is a viable tool for mapping soil salinity in agricultural lands

2017 ◽  
Vol 71 (4) ◽  
pp. 231-238 ◽  
Author(s):  
Elia Scudiero ◽  
Dennis L. Corwin ◽  
Ray G. Anderson ◽  
Kevin Yemoto ◽  
Wesley Clary ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Agricultural Lands

scholarly journals Mapping Soil Salinity in Western United Arab Emirates (UAE) Using Satellite Data

2018 ◽  
Author(s):  
Abdelgadir Abuelgasim ◽  
Rubab Ammad
Keyword(s):  
United Arab Emirates ◽  
Soil Salinity ◽  
Crop Yields ◽  
Agricultural Soils ◽  
Image Data ◽  
Saline Soils ◽  
Landsat 8 ◽  
Agricultural Lands ◽  
Infra Red ◽  
Semi Arid

Soil salinity, whether natural or human induced, is a major geo-hazard in arid and semi-arid landscapes. In agricultural lands, it negatively affects plant growth, crop yields, whereas in semi-arid and arid non-agricultural areas it affects urban structures due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation Accurately mapping soil salinity through remote sensing techniques has been an active area of research in the past few decades particularly for agricultural lands. Most of this research has focused on the utilization and development of salinity indices for properly mapping and identifying saline agricultural soils. This research study develops a soil salinity index and model using Landsat 8 OLI image data from the near infra-red and shortwave infra-red spectral information with emphasis on soil salinity mapping and assessment in non-agricultural desert arid and semi-arid surfaces. The developed index when integrated into a semi-empirical model outperformed in its soil salinity mapping overall accuracy (60%) in comparison to other salinity indices (~50%). The newly developed index further outperformed other indices in its accuracy in mapping and identifying high saline soils (67%) and excessively high saline soils (90%).

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