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Combination of spectral indices of OLI and TIRS sensor and magnetic induction data in order to estimate the spatial variation of soil salinity | ||
Desert | ||
مقاله 8، دوره 26، شماره 2، اسفند 2021، صفحه 251-265 اصل مقاله (914.78 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jdesert.2021.318414.1006806 | ||
نویسندگان | ||
H.R. Matinfar* 1؛ N. Kianain2؛ S. Ahmadi3 | ||
1Dept. of Soil science, faculty of Agriculture, Lorestan university. | ||
2Department of soil science, Faculty of Agriculture, Lorestan University | ||
3graduated with an MSc of soil Science | ||
چکیده | ||
Soil salinity and alkalinity are among the most important soil degradation processes, especially in arid and semi-arid regions. The purpose of this study is to evaluate spectral indicators as well as use the data of the EM38 for identifying saline soils and spatial changes. The study area is Ghahavand plain that is located in Hamedan Province. In this study, Landsat 8 satellite data were used. Soil sampling of 37 points was performed and 86 points were read using an electromagnetic induction device. Using protomorphic units based on visual interpretation of OLI 543 false-color composite image and field observations, a total of 9 homogeneous units were identified in the region using these units as training regions for supervised classification. The results showed that the detection of soil salinity in the visible spectrum (blue, green, and red band) is feasible. The bands 5, 6, and 7 can be useful in differentiating salty white crust lands from salty gray crust lands. In the reflective bands, the white and smooth crust exhibits the highest reflectance. The results of classification accuracy showed that the highest total accuracy was 90.0 and the kappa coefficient was 80.45 when bands 1, 2, 3, 4, 5, 6, 7, 10, and 11 were used and shallow and abandoned plowed lands had the least accuracy. Also, the final model of salinity estimation showed that SI6 and SI11 indicators and electromagnetic induction vertical measurements (EMv) are the most suitable variables for estimating salinity spatial changes. | ||
کلیدواژهها | ||
Soil salinity؛ Landsat 8؛ spectral indices؛ magnetic induction | ||
مراجع | ||
References Abbas A, Khan S, Hussain N, Hanjra MA, Akbar S. 2013. “Characterizing soil salinity in irrigated agriculture using a remote sensing approach.” Physics and Chemistry of the Earth Parts A/B/C, vol. Abbas A, Khan S. 2007. Using remote sensing techniques for appraisal irrigated soil salinity. International Congress on Modelling and Simulation (MODSIM), Modelling and Simulation Society of Australia and New Zealand, Brighton. AbdelRahman M, Metwaly M, Shalaby A. 2019. Quantitative assessment of soil saline degradation using remote sensing indices in Siwa Oasis. Remote Sensing Applications: Society and Environment 13 (2019) 53–60. Abuelgasim A, Ammad R. 2017. Mapping Sabkha Land surfaces in the United Arab Emirates (UAE) using Landsat 8 data, principal component analysis and soil salinity information. Int. J. Eng. Manuf. 7 (4). Abuelgasim A, Ammad R. 2019. Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data, Remote Sensing Applications: Society and Environment 13 (2019) 415–425. Alavi Panah S. 2003. Application of remote sensing in Geoscience (Soil Science). University of Tehran, Samt Publications. p. 67-134. Alavi Panah S. 2006. Remote sensing, principles and application. University of Tehran, Samt Publications. Allbed A, Kumar L, Aldakheel Y. 2014. "Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region." Geoderma 230: 1-8. Allbed A, Kumar L. 2013. Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Remote Sens 2(December):373–385. https://doi.org/10.4236/ ars.2013.24040. Asfaw E, Suryabhagavan KV, Argaw M. 2016. Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia.” Journal of the Saudi Society of Agricultural Sciences. Azabdaftari A, Sunarb F. 2016. “Soil salinity mapping using multitemporal Landsat data.” in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic. Barbiero L, Cunnac S, Mana L, Laperrousaz C, Hammecker C, Maeght JL. 2001. "Salt distribution in the Senegal middle valley: Analysis of a saline structure on planned irrigation schemes from N’Galenka creek." Agricultural Water Management 46(3): 201-213. Day RP. 1965. Pipette method of particle size analysis. In: Methods of soil analysis. Agronomy 9.ASA USA.p. 553-562. Douaoui A, Herve N, Walter C. 2006. "Detecting salinity hazards within a semiarid context by means of 265 Matinfar et al. combining soil and remote-sensing data." Geoderma 134(1): 217-230. El Harti A, Lhissou R, Chokmani K, Ouzemou J. 2016. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. Int J Appl Earth Obs Geoinf 50:64–73, Elsevier B.V. https://doi.org/10. Farifteh J, George RJ. 2006. "Assessing salt-affected soils using remote sensing, solute modelling, and geophysics." Geoderma 130(3): 191-206. Fernandez-Buces N, Palacio JL. 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(4): 644-667. Forkuor G, Hounkpatin OKL, Welp G, Thiel M. 2017. High Resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLoS One 12,e0170478. Hafyani M, Essahlaoui A, Baghdadi M. Teodoro A, Mohajane M, Hmaidi A, Ouali A. 2019. Modeling and mapping of soil salinity in Tafilalet plain (Morocco). Arabian Journal of Geosciences. 12:35. Huang J, Wong VNL, Triantafilis J. 2014. Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data.soil use and management. 30, 394–402 Ibrahim M. 2016. Modeling soil salinity and mapping using spectral remote sensing data in the arid and semi-arid region. Int J Remote Sens Appl 6:76. Johnston M, Savage MJ, Moolman JH, Plessis HM. 1996. "Calibration models for interpretation of soil salinity measurements using an electromagnetic induction technique." South African Journal of Plant and Soil 13(4): 110-114. Khan NM, Shiozawa S. 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators." Agricultural Water Management 77(1): 96-109. Kumar N, Singh SK, Pandey HK. 2018. Appl. Geomath. https://doi.org/10.1007/ s12518-018-0218-2. Lagacherie P, Baret F, Feret JB, Netto JM, MarcRobbez-Masson J. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112 (3: 825-83. Matinfar HR, Alavi Panah SK, Zand F, Khodaei K. 2011. Detection of soil salinity changes and mapping land cover types based upon remotely sensed data. Arab J Geosci. Mougenot B. 1993. Effects des sels sur la réflectance et télédétection des sols salés." Cahiers ORSTOM, Serie Pedologie 28: 45-54. Nawar S, Buddenbaum H, Hill J, Kozak J. 2014. “Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS).” Remote Sensing, vol. 6, pp. 10813-10834 Rahimian M, Hashminezhad Y. 2010. Calibration of Electromagnetic Inductor (EM38) for evaluation of soil salinity. Journal of Soil Research (Soil and Water Sciences). Volume 24, No. 3. 2010. p. 243-252. Rahmati M, Hamzepour N. 2017. Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data. International Journal of Remote Sensing. 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20. Rao B, Sankar T, Dwivedi R, Thammappa S, Venkataratnam L, Sharma R, Das S. 1995. Spectral behaviour of salt-affected soils. Inter. J. Rem. Sens. 16: 2125-2136. Rongjiang R, Jingsong Y. 2010. Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method. AgriculturalWater Management.97:1961-1970 Rouse Jr, John W. 1974. "Monitoring the vernal advancement of retrogradation of natural vegetation, Final report, Greenbelt, MD." National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC). Sharma D, Gupta S. 2000. Application of EM38 for soil salinity appraisal: an Indian experience. EM38 Workshop, New Delhi, India. Song CC, Yan BX, Song XS. 2002. The electromagnetism technology application in the sodium-saline soil. Scientia Geographica Sinica, 22(1), 91-95, (in Chinese). DESERT 2021, 26(2): 251-265 266 Thomas DSG. 1997. Arid zone Geomorphology: Process, Form and Change in Drylands (Second edition). John Willey & Sons Inc. 713 pp. Tripathi NK, Rai BK, Dwivedi P. 1997. Spatial modeling of soil alkalinity in GIS environment using IRS data. Proceedings of the 18th Asian conference on remote sensing, Kualalampur, pp. 1–6. Walky A, Black I. 1934. An examination of the Degtiareff method for determining soil organic matter and proposed modification of the chromic acid titration method. Soil Science, 63, 29-38. Williams BG, Baker GC. 1982. An electromagnetic induction technique for reconnaissance surveys of soil salinity hazards. Australian Journal of Soil Research, 20, 107–118. Yu R, Liu T, Xu Y, Li C. 2010. "Analysis of salinization dynamics by remote sensing in Hetao IrrigationDistrict of North China." Agricultural Water Management 97(12): 1952-1960 | ||
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