IDEAS home Printed from https://ideas.repec.org/a/caa/jnlswr/v17y2022i1id178-2020-swr.html
   My bibliography  Save this article

Assessment and mapping of soil salinity using electromagnetic induction and Landsat 8 OLI remote sensing data in an irrigated olive orchard under semi-arid conditions

Author

Listed:
  • Mohamed Elhedi Gharsallah

    (Laboratory of Agricultural Production Systems and Sustainable Development, National Agronomic Institute of Tunisia, University of Carthage, Tunis, Tunisia)

  • Hamouda Aichi

    (Laboratory of Agricultural Production Systems and Sustainable Development, Higher School of Agriculture Education, University of Carthage, Tunis, Tunisia)

  • Talel Stambouli

    (Laboratory of Agricultural Production Systems and Sustainable Development, Higher School of Agriculture Education, University of Carthage, Tunis, Tunisia)

  • Zouhair Ben Rabah

    (National Center for Mapping and Remote Sensing, Ministry of National Defense, Tunis, Tunisia)

  • Habib Ben Hassine

    (Laboratory of Agricultural Production Systems and Sustainable Development, Higher School of Agriculture Education, University of Carthage, Tunis, Tunisia)

Abstract

Salinisation threatens the sustainability of irrigated olive orchards in Tunisia. Electromagnetic induction measurements and soil spectral index calculations could help to survey the soil salinity. This study aimed to map changes in the soil salinity spatial pattern using geostatistical techniques and soil spectral index regression. The study area is located in Sminja, Tunisia. It is a 665 ha olive orchard, landscaped in ridges and furrows and managed following a very high-density planting system (1.5 × 4 m2). Electromagnetic readings measured in situ with an electromagnetic device (EM38) that was fitted, in turn, to the electrical conductivity of the saturated paste of five soil depths namely: 0-20, 20-40, 40-60, 60-80 and 80-100 cm and to the average electrical conductivity of the saturated paste of the 0-100 cm soil depth. Both the ordinary kriging and universal kriging performed similarly and well in mapping the soil salinity. (R2= 0.86 and 0.89 for the 0-20 cm and the 0-100 cm depths, respectively). Our results prove that mapping the soil salinity based on electromagnetic induction and kriging methods is an effective approach, which allows one to monitor the soil salinity within permanent croplands in semi-arid conditions. Salinisation that reaches intolerable values by olive trees, is especially accumulated on the top of the ridges, where the drippers are installed. Furthermore, based on two Landsat 8 images acquired on April 30, 2019 and May 16, 2019, respectively, we calculated seven soil spectral indices. Nevertheless, multiple regression models between the electromagnetic readings and various combinations of soil spectral indices were poor. In the coming investigations, under permanent land cover, spectral index regression models should integrate not only the soil, but also vegetation indices.

Suggested Citation

  • Mohamed Elhedi Gharsallah & Hamouda Aichi & Talel Stambouli & Zouhair Ben Rabah & Habib Ben Hassine, 2022. "Assessment and mapping of soil salinity using electromagnetic induction and Landsat 8 OLI remote sensing data in an irrigated olive orchard under semi-arid conditions," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 17(1), pages 15-28.
  • Handle: RePEc:caa:jnlswr:v:17:y:2022:i:1:id:178-2020-swr
    DOI: 10.17221/178/2020-SWR
    as

    Download full text from publisher

    File URL: http://swr.agriculturejournals.cz/doi/10.17221/178/2020-SWR.html
    Download Restriction: free of charge

    File URL: http://swr.agriculturejournals.cz/doi/10.17221/178/2020-SWR.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/178/2020-SWR?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamideh Nouri & Sattar Chavoshi Borujeni & Sina Alaghmand & Sharolyn J. Anderson & Paul C. Sutton & Somayeh Parvazian & Simon Beecham, 2018. "Soil Salinity Mapping of Urban Greenery Using Remote Sensing and Proximal Sensing Techniques; The Case of Veale Gardens within the Adelaide Parklands," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    2. Billal Hossen & Helmut Yabar & Md Jamal Faruque, 2022. "Exploring the Potential of Soil Salinity Assessment through Remote Sensing and GIS: Case Study in the Coastal Rural Areas of Bangladesh," Land, MDPI, vol. 11(10), pages 1-18, October.
    3. Shuai Li & Pu Guo & Fei Sun & Jinlei Zhu & Xiaoming Cao & Xue Dong & Qi Lu, 2024. "Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China," Land, MDPI, vol. 13(6), pages 1-20, June.
    4. Sheshu Zhang & Jun Zhao & Jianxia Yang & Jinfeng Xie & Ziyun Sun, 2024. "Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China," Land, MDPI, vol. 13(6), pages 1-21, June.
    5. Yasin ul Haq & Muhammad Shahbaz & H. M. Shahzad Asif & Ali Al-Laith & Wesam H. Alsabban, 2023. "Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    6. repec:caa:jnlswr:v:preprint:id:5-2024-swr is not listed on IDEAS
    7. Romeu Gerardo & Isabel P. de Lima, 2022. "Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal," Agriculture, MDPI, vol. 12(9), pages 1-20, September.
    8. Maxime Dumont & Guilhem Brunel & Paul Tresson & Jérôme Nespoulous & Hassan Boukcim & Marc Ducousso & Stéphane Boivin & Olivier Taugourdeau & Bruno Tisseyre, 2024. "Operational sampling designs for poorly accessible areas based on a multi-objective optimization method," Post-Print hal-04566087, HAL.
    9. Hui Deng & Wenjiang Zhang & Xiaoqian Zheng & Houxi Zhang, 2024. "Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image," Agriculture, MDPI, vol. 14(4), pages 1-17, March.
    10. Shuoyang Li & Guiyu Yang & Cui Chang & Hao Wang & Hongling Zhang & Na Zhang & Zhigong Peng & Yaomingqi Song, 2024. "Remote Sensing Inversion of Salinization Degree Distribution and Analysis of Its Influencing Factors in an Arid Irrigated District," Land, MDPI, vol. 13(4), pages 1-18, March.
    11. Weitao Lv & Xiasong Hu & Xilai Li & Jimei Zhao & Changyi Liu & Shuaifei Li & Guorong Li & Haili Zhu, 2024. "Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 16(9), pages 1-21, April.
    12. Min Ma & Yi Hao & Qingchun Huang & Yongxin Liu & Liancun Xiu & Qi Gao, 2024. "Soil Salinity Estimation by 3D Spectral Space Optimization and Deep Soil Investigation in the Songnen Plain, Northeast China," Sustainability, MDPI, vol. 16(5), pages 1-26, March.
    13. Achivir Stella Yawe & Changlai Xiao & Oluwafemi Adewole Adeyeye & Mingjun Liu & Xiaoya Feng & Xiujuan Liang, 2022. "Spatio-Temporal Evolution of the Ecological Environment in a Typical Semi-Arid Region of Northeast China," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    14. Azamat Suleymanov & Ilyusya Gabbasova & Mikhail Komissarov & Ruslan Suleymanov & Timur Garipov & Iren Tuktarova & Larisa Belan, 2023. "Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas," Agriculture, MDPI, vol. 13(5), pages 1-11, April.
    15. Tharani Gopalakrishnan & Lalit Kumar, 2020. "Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    16. Zixuan Zhang & Beibei Niu & Xinju Li & Xingjian Kang & Zhenqi Hu, 2022. "Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China," Land, MDPI, vol. 11(12), pages 1-21, December.
    17. Yingxuan Ma & Nigara Tashpolat, 2023. "Current Status and Development Trend of Soil Salinity Monitoring Research in China," Sustainability, MDPI, vol. 15(7), pages 1-25, March.
    18. Jiawen Hou & Mao Ye, 2022. "Effects of Dynamic Changes of Soil Moisture and Salinity on Plant Community in the Bosten Lake Basin," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    19. Hesham M. Aboelsoud & Mohamed A. E. AbdelRahman & Ahmed M. S. Kheir & Mona S. M. Eid & Khalil A. Ammar & Tamer H. Khalifa & Antonio Scopa, 2022. "Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management," Land, MDPI, vol. 11(7), pages 1-19, July.
    20. Iqra Farooq & Shabir Ahmed Bangroo & Owais Bashir & Tajamul Islam Shah & Ajaz A. Malik & Asif M. Iqbal & Syed Sheraz Mahdi & Owais Ali Wani & Nageena Nazir & Asim Biswas, 2022. "Comparison of Random Forest and Kriging Models for Soil Organic Carbon Mapping in the Himalayan Region of Kashmir," Land, MDPI, vol. 11(12), pages 1-15, December.
    21. Dauren Rakhmanov & Bořivoj Šarapatka & Kamilla Alibekova & Jan Černohorský & Petr Hekera & Zhassulan Smanov, 2024. "Assessment of agricultural land salinization via soil analysis and remote sensing data: Case study in Pavlodar region, Kazakhstan," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 19(2), pages 111-121.

    More about this item

    Keywords

    EM38; geostatiscs; kriging; olive grove; soil salinisation; Tunisia;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlswr:v:17:y:2022:i:1:id:178-2020-swr. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.