Monitoring and Evaluation of Spatial Variations in Soil Electrical Conductivity Using Remote Sensing

Document Type : Research Article

Authors

1 Associate Professor, Department of Meteorology, Faculty of Humanities, Zanjan University, Iran

2 Assistant Professor, Department of Meteorology, Faculty of Humanities, Zanjan University, Iran

3 PhD Student in Meteorology, Faculty of Humanities, Zanjan University, Iran

Abstract

Soil salinity is an important environmental issue that reduces soil productivity. For optimal management of soil resources, quantitative monitoring of soil salinity, temporal changes and spatial analysis of the factors affecting it are necessary. The purpose of this study is to extract the soil salinity distribution map and its spatial analysis after more than normal rainfall in the rainy year of 1997-98 in western Iran. Using Landsat satellite imagery and GDVI index and algorithm written in Google Engine system, soil electrical conductivity map was extracted and classified into five salinity classes. The results showed that in general, soil salinity has decreased in the study area. Areas with high salinity that are in the low altitude class have not changed. If the precipitation factor in this study period is the most important factor in changes in salinity distribution, this factor could not have a great effect on the salinity class, but the medium salinity class had the most changes. Move the soil surface of this class down. For the ellipse, three times the standard spatial deviation of the northwest to the southeast was obtained, which shows that more than 99% of the salinity dispersion follows the spatial arrangement of altitudes, precipitation and dispersion of soil categories in this direction. Statistics of 0.4566 of Moran index and P_Value showed 0.00 spatial salinity of soil salinity in the west of the country. The hot-spot map also showed that the surface salinity of the soil is clustered in the northwest and southeast directions at altitudes of less than 1200 meters. Hot-spot analysis also showed that soil salinity to the east and inland has found more clustering pattern. The results and methods of this research can easily and quickly identify areas that are exposed to soil salinity and could be used in environmental planning to implement preventive measures. The results of this research are also used and its outputs are also utilized to identify soil salinity centers in agricultural planning and allocation of facilities.

Keywords

References

[1]. Akça P, Aydin M, Kapur S, Kume T, Nagano T, Watanab S, Çilek A Zorlu K. Long-term monitoring of soil salinity in a semi-arid environment of Turkey.2020;19:104614.
[2]. Daliakopoulos N, Tsanis, K, Koutroulis A, Kourgialas N, Varouchakis A, Karatzas P, Ritsema C. The threat of soil salinity: A European scale review. Sci. Total Environ.2016; 573:727–739.
[3]. Tanji K. Salinity in the Soil Environment. In Salinity: Environment-Plants-Molecules. Dordrecht. The Netherlands. 2004; pp. 21–51.
[4]. Abuelgasima A, Ammad R. Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data, Remote Sensing Applications: Society and Environment.2019; 13 (2019): 415–425.
[5]. Zheng H, Schroder J, Pittman J, Wang J, Payton ME. Soil salinity using saturated paste and 1:1 soil to water extracts. Soil Sci Soc Am J.2005; 69(4):1146–1151
[6]. Allbed A, Kumar L. Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review. Advances in Remote Sensing.2013; 2: 373-385
[7]. AlKhair F. Soil Salinity Detection Using Satellite Remote Sensing. Submitted to the international institute for Geo- information science and earth observation in partial fulfillment of the requirements for the degree of Master of Science in geo- information science and earth observation. Netherland.2003.P70.
[8]. Meternicht G, Zink j. Remote Sensing of Soil Salinity: Potentials and Constraints. Remote Sensing of Environment.2003; 5812:1-20.
[9]. Ghabour T, Daels, L. Mapping andmonitoring of soil salinity of ISSN. Egypt. J. Soil Sci.1993; 33 (4): 355–370.
[10]. Ivushkina K, Bartholomeusa H, Bregta A, Alim K, Bas S. 2019, Global mapping of soil salinity change, Remote Sensing of Environment.2019;231(15):111260.
[11]. Fourati T, Bouaziz H, Benzina M. Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques. Environ Monit Assess.2017; 189, 177. https://doi.org/10.1007/s10661-017-5877-7
[12]. Jiang H., Shu H. Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis, Xinjiang, China. Earth Sci Inform. 2019; 12: 43–56.
[13]. https://doi.org/10.1007/s12145-018-0358-2
[14]. Koshal K. Spectral Characteristics of Soil Salinity Areas in Parts of South-West Punjab through Remote Sensing and GIS, International Journal of Remote Sensing and GIS, 2012; 1, (2): 84-89.
[15]. Sharma R, Mondal A. K. Mapping of soil salinity and sodicity using digital image analysis and GIS in irrigated lands of the Indo-Gangetic plain. Agropedology, 2006; 16: 71–76.
[16]. WENG L, GONG P, ZHU Z. A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data, Pedosphere, 2010; 20(3): 378-388.
[17]. Khadim F, Su H, Xu L, Tian J. Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance, Physics and Chemistry of the Earth,2019;31-50.
[18]. Nguyen K, Liou Y, Tran H. Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta Vietnam. Prog Earth Planet Sci.2020; 7(1):1-16 https://link.springer.com/article/10.1186/s40645-019-0311-0
[19]. Dashtakian K, Pakparvar M, Abdolahi J. Investigation of soil salinity mapping methods using Landsat satellite data in Marvast region, Iran Rangeland and Desert Research. 2009; 15(2): 139-157. [Persian]
[20]. Khanamani A, Sangoi R, Shabazi H. Assessment of soil condition using remote sensing technology and GIS (Case study of Segzi plain of Isfahan). Journal of Remote Sensing and GIS Application in Natural Resources Sciences. 2011; 3: 25-37. [Persian]
 
[21]. Daempanah R, Haghnia GH, Alizadeh A, Karimi A. Preparation of salinity and sodium map of surface soil by telemetry and geostatistical methods in the south of Mahallat city, Journal of Water and Soil. 2011; 3(25): 498-508. [Persian]
[22]. Nohegar A, Zareh GH. Extraction of soil salinity zones in arid and semi-arid regions using remote sensing data (Case study: Darab city). Journal of Geography and Environmental Hazards. 2012; 1: 49-64. [Persian]
[23]. Emani m, Bahrami H, Askoii S. Estimation of soil electrical conductivity using hyperion spectral images, Case study: North of Urmia plain. Iranian Soil and Water Research.2014; 45(1): 67-74. [Persian]
[24]. Momopour M. Investigation of temporal and spatial changes of soil salinity in Abadan city in a 24-year period with satellite images of geography and environmental stability.2018; 8(27): 47-58. [Persian]
[25]. Gamarkachi A, Akbari M, Hasangholi A, Yonesi M. Monitoring of soil salinity and vegetation using multispectral remote sensing data in the Zardasht saline drainage area of Qazvin.2020; 34: 37-52. [Persian]
[26]. KHademi F, Pirkhayati H, SHahkarami S. Study of increasing saline soils around Lake Urmia using GIS and RS, Earth Sciences.2016; 24 (94): 93-98. [Persian]
[27]. Zinali M, Jafarzadeh A, Farzin O, Valizadehkamran KH. Evaluation of surface soil salinity by pixel based method based on TM sensor data (case study of lands of Khoy city, West Azerbaijan province), geographical information.2017; 25 (66): 127-139. [Persian]
[28]. Alavipanah K. Application of Remote Sensing in Earth Sciences (Soil Science) Tehran: University of Tehran.2014: P.411. [Persian]
[29]. Moradian S, Nabiolahi K, Tahizade K, Merjordi R. Prediction of soil salinity using tree regression and neural network in Qorveh region of Kurdistan province, Journal of Soil Management and Sustainable Production.2017; 7(4): 115-129. [Persian]
[30]. Mudler L, Bruin S, Schaepman M. Representing Major Soil Variability at Regional Scale by Constrained Latin Hypercube Sampling of Remote Sensing Data. International Journal of Applied Earth Observation and Geoinformation.2013; 21: 301-310.
[31]. Singh P, Srivastav K. Mapping of waterlogged and salt affected soils using microwave radiometers. Int. J. Remote Sens.2007; 11: 1879–1887.
[32]. Mougenot B, Pouget M, Epema G. Remote sensing of salt-affected soils. Remote Sens. Rev.1993; 7: 241–259.
[33]. Metternicht G, Zinck. Remote sensing of soil salinity: potentials and constraints. Remote Sen. Environ.2014; 85:1-20.
[34]. Abrifam M. the Synoptic Analysis of Entranced Air Masses to the West if Iran (2004-2005), Supervisor: Gholamreza Barati, Master of Science in Climatology. Razi University of Kermanshah.2001. P.162.
[35]. Alijani B. Synoptic Climatology. Samat Publications.Tehran.2002:268. [Persian]
[36]. Mojarad F, Masompour J. Estimation of maximum probable precipitation by synoptic method in Kermanshah province. Geographical studies of arid regions.2013; 13: 1–14. [Persian]
[37]. Wu W. The generalized difference vegetation index (GDVI) for dryland characterization. Remote Sens.2014; 6:1211–1233.
[38]. Alijani B. Spatial Analysis, Journal of Spatial Analysis of Environmental Hazards.2014; 3(1):1-14. [Persian]
[39]. Harvey D. Explanation in Geography. Arnold, London;1969. P.284..2014; 3(1):1-14.
[40]. Asakereh H, Sifipour Z. Spatial modeling of annual rainfall in Iran, Geography and Development. 2015; 10(29):15-30. [Persian]
[41]. Asakereh H, SHademan H. Identifying the spatial relationships of pervasive hot days in Iran. Journal of Geographical Research. 2015; 116(1):54-70. [Persian]
[42]. Dai X, Guo Z, Zhang L, Li D. (2010). Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, Environ Res Risk Assess.2010; 24: 247–257.
[43]. Gail M, Krickeberg K, Samet J, Tsiatis A, Wong W. Statistics for Biology and Health. USA. Springer; 2007. P.402.
[44]. Fischer M, Manfred M. Spatial Analysis and Geocomputation. Germany. Springer; 2006.P. 344.
Iranian journal of Ecohydrology
Volume 7, Issue 4
January 2021
Pages 1113-1126

Files

History

  • Receive Date: 20 August 2020
  • Revise Date: 02 November 2020
  • Accept Date: 02 November 2020

Share

How to cite

Statistics