Evaluation of the Efficiency of Spectral Data and Indices Derived from OLI and TIRS Sensors in Estimating Soil Salinity in Arid Regions of Southern Ilam Province

Document Type : Original Article

Authors

1 Associate Prof. of Soil Science, College of Agriculture, Lorestan University, Lorestan

2 Ph.D. Student of Soil Science, College of Agriculture, Lorestan University, Lorestan

3 Graduate Master of Soil Science, College of Agriculture, Ilam University, Ilam

Abstract

Soil salinity is one of the most important environmental problems, and the identification and zoning
of saline soils is difficult due to the need for sampling and laboratory analysis, as well as having
temporal and spatial variability. In recent years, the use of satellite imagery has always been of
interest to experts due to its ease of use and ability to detect phenomena. Remote sensing information
greatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study,
220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according to
the type of study and physiographic types and soil units. Then, pH and EC values were measured
using standard methods. Soil salinity values were evaluated using correlations between EC electrical
conductivity values obtained from ground data and variables obtained from Landsat 8 satellite images
including bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinity
estimation model was obtained using stepwise regression method. This method involves the automatic
selection of independent variables, and with the availability of statistical software packages, it is
possible to do so even in models with hundreds of variables. In previous studies, indicators and bands
have been used separately and in a limited way, but in this study, an attempt has been made to use a
combination of different indicators more widely, and finally to achieve the best relationship by
eliminating the indicators that have the least impact on soil salinity estimation. Using significant level
analysis and correlation between the output of models and ground data, the best model with a value of
(R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, the
highest area belonged to non-saline class which comprises 75% of the total study area and about 1%
of the soils belong to the saline class.

Keywords


اخضری د.، می‌آبادی اسدی، ا.، 1395، تهیة نقشة شوری خاک با استفاده از تحلیل طیفی داده‌های سنجندة OLI و داده‌های میدانی. مطالعة موردی: جنوب دشت ملایر، سنجش از دور و سامانة اطلاعات جغرافیایی در منابع طبیعی، سال هفتم، شمارة 2، صص. 87-1.
چیت‌ساز.، و.، 1378، بررسی امکان تهیة نقشة شوری و قلیائیت خاک در منطقة شرق اصفهان با استفاده از داده‌های رقومی TM، پایان‌نامة کارشناسی ارشد، دانشگاه صنعتی اصفهان، دانشکدة منابع طبیعی.
دشتکیان، ک.، پاک‌پرور، م.، عبدالهی، ج.، 1387، بررسی روش‌های تهیة نقشة شوری خاک با استفاده از داده‌های ماهواره‌ای لندست در منطقة مروست، تحقیقات مرتع و بیابان ایران، شمارة 2، صص. 157-139.
دماوندی ع.ا.، درویش‌صفت، ع.ا.، 1378، بررسی امکان کاربرد داده‌های ماهواره‌ای در شناسایی و طبقه‌بندی اراضی شور، به‌روش رقومی، همایش نقشه‌برداری.
زینالی م.، جعفرزاده، ا.، شهبازی، ف.، اوستان، ش.، ولی‌زاده، ک.، 1395، ارزیابی شوری خاک سطحی با روش پیکسل‌مبنا براساس داده‌های سنجندة TM. مطالعة موردی: اراضی شهرستان خوی، استان آذربایجان‌غربی، اطلاعات جغرافیایی، شمارة 25 (99)، صص. 139-127.
علوی‌پناه.، س.ک، 1382، کاربرد سنجش از دور در علوم زمین (علوم خاک)، انتشارات دانشگاه تهران.
کافی م.، برزئی، ا.، صالحی، م.، کمندی، ع.، معصومی، ع.، نباتی، ج.، 1388، فیزیولوژی تنش‌های محیطی در گیاهان، مشهد: انتشارات جهاد دانشگاهی مشهد.
متین‌فر، ح.، سرمدیان، ف.، علوی‌پناه، س.ک.، 1388، ارزیابی داده‌های سنجندة IRS-1D به‌منظور شناسایی خاک‌ها براساس مطالعات میدانی و به‌کمک سامانه‌های اطلاعات جغرافیایی (GIS) در منطقة آران و بیدگل، مهندسی و مدیریت آبخیز، دورة بیست‌ویکم، شمارة 1 (پیاپی 82)، صص. 58-46.
Abdelfattah, M.A., 2009, Soil Salinity Mapping Model Developed Using RS and GIS – A Case Study from Abu Dhabi, United Arab Emirates, European Journal of Scientific Research, 26(3), PP. 342-351.
Al-Hassoun, S.A., 2012, Remote Sensing of Soil Salinity in an Arid Areas in Saudi Arabia, International Journal of Civil and Environmental Engineering IJCEEIJENS, 10(2), PP. 11-20
Asfaw, E., Suryabhagavan, K.v. & Argaw, M., 2016, Soil Salinity Modeling and Mapping Using Remote Sensing and GIS: The Case of Wonji Suger Cane Irrigation Farm, Ethiopia, Jounal of the Saudi Society of Agricultural Sciences, PP. 1-22.
Azabdaftari, A. & Sunar, f., 2016, Soil Salinity Mapping Multitemporal Land Sat Data, The International Archives of the Potogrammetry, Remote Sensing and Spatial Imnformation Sciences, xl-B7, PP. 809-813.
Buces, F.N., Siebe, C., Cram, S. & Palacio, J.L., 2006, Mapping Soil Salinity Using a Combined Spectral Response Index for Bare Soil and Vegetation: (A Case Study in the Former Lake Texcoco, Mexico), Journal of Arid Environments, 65, PP. 644-667.
Dennis, L., 2018, Validating the Use of MODIS Time Series Fore Salinity Assessment over Agricultural Soils in California, USA, Ecological Indicators, 93, PP. 889-898.
ELHarti, A., Lhissou, R., Chokmani, K., Ouzemou, J., Hassouna, M., Bachaoui, E. & Ghmari, A., 2016, Spatiotempral Monitoring of Soil Salinization in Irrigated Tadla Plain (Morrocco) Using Satellite Spectral Indices, International Journal of Applied Earth –Observation and Geoinformation, 50, PP. 64-73.
 
Helmut  L., Tavakoli, H., Ansair R., Askar, H. & Rastegari, J., 2013, Crop and Forage Production Using Saline Waters, Daya Publishing House, India.
Kant, C., Aydin, A. & Turan, M., 2008, Ameliorative Effect of Hydro Gel Substrate on Growth, Inorganic Ions, Proline and Nitrate Contents of Bean under Salinity Stress, Journal of Plant Nutrition, 31(7), PP. 1420-1439.
Landsat-8 (L8)., 2016, Data User Handbook, Landsat Project Science Office at NASA's Goddard Space Flight Center in Greenbelt, 1168, Available at:
Mahmoudi, S., Mohammadkhani, A. & Rouhi, V., 2016, Effects of Sodium Chloride and Calcium Chloride on Growth, Gel Content and Concentration of Some Nutrients in Aloe Vera under Greenhouse Conditions, Journal of Science and Technology of Greenhouse Culture, 7(2), PP. 85-97.
McLean, E.D., 1982, Soil pH and Lime Requirement, In: A.L. Page (Editor), Methods of soil analysis. Part 2, 2nd ed, Agronomy Monograph, 9. ASA and SSSA, Madison, WI, PP. 199-224.
Metternicht, G.I. & Zinck, J.A., 2003, Remote Sensing of Soil Salinity: Potentials and Constraints, Remote Sensing of Environment, 85, PP. 1-20.
Metternicht, G.I. & Zinck, J.A., 2008, Remote Sensing of Soil Salinization: Impact on Land Management, CRC Press.
Metternicht, G.I. & Zinck, J.A., 2009, Remote Sensing of Soil Salinization: Impact on Land Management, CRC Press, Taylor & Francis Group, LLC.
Qureshi, A.S., Qadir, M., Heydari, N., Turral, H. & Javadi, A.A., 2007, Review of Management Strategies for Saltprone Land and Water Resources in Iran, Working paper 125, International Water Management Institute, Sri Lanka.
Rekha, S., Jenita, R., Mrunalini, B., Kannan, V. & Nethaji Mariappan, V.E., 2011, Development and Demonstration of Satellite Image Salinity Analyzer-A Tool for Salinity Mapping, International Journal on Applied Bioengineering, 5(1), PP. 25-29.
Rhoades, J.D., 1982, Cation Exchangeable Capacity, In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis: Part2. Chemical and Micro-biological Properties. Agronomy Monograph, 9. ASA and SSSA, Madison, WI, PP. 149-157.
Salman, A. & Mubeen-Ul-Din, A., 2000, Using State of the Art RS and GIS for Monitoring Water Logging and Salinity,  Proceeding of a Roundtable Meeting, Lahor, Pakistan, 10-11 Nov. 2000, IPTRID: FAO.
Soil Survey Staff, 1999, Soil Taxonomy: A Basic System of Soil Classification for Making and  Interpreting Soil Surveys, USDA, Hand Book. 436. 2nd ed. Washington, DC, U.S.A.
Soil Survey Stuff,  2010,  Keys to Soil Taxonomy, USDA, NRCS.
Torabi, M, 2014, Physiological and Biochemical Responses of Plants to Salt Stress, The 1st International Conference on NEW IDEAS in Agriculture, 26-27 Jan., Islamic Azad University Khorasgan Branch, Isfahan, Iran.
United States Salinity Laboratory (USSL), 1954, Diagnosis and Improvement of Saline and Alkali Soils, USDA Hand Book, PP. 60-147.
Wilcox, L.V., 1955, Classification and Use of Irrigation Waters, US Department of Agriculture. Cire., 969, Washington D.C. USA. P. 19.
Whitney, K., Scudiero, E., El-Askary, H. M., Skaggs, T. H., Allali, M., & Corwin, D. L., 2018, Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecological indicators, 93, 889-898.‏
Zeng, W., Zhang, D., Fang, Y., Wu, J. & Huang, J., 2018, Comparison of Partial Least Square Regression, Support Vector Machine, and Deep-Learning Techniques for Estimating Soil Salinity from Hyperspectral Data, Journal of Applied Remote Sensing, 12(2), P. 022204