IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p2069-d1349905.html
   My bibliography  Save this article

Soil Salinity Estimation by 3D Spectral Space Optimization and Deep Soil Investigation in the Songnen Plain, Northeast China

Author

Listed:
  • Min Ma

    (Chinese Academy of Geological Sciences, Beijing 100037, China
    Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 100024, China
    School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
    Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing 100055, China)

  • Yi Hao

    (Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 100024, China)

  • Qingchun Huang

    (Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 100024, China)

  • Yongxin Liu

    (Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 100024, China)

  • Liancun Xiu

    (Chinese Academy of Geological Sciences, Beijing 100037, China)

  • Qi Gao

    (Hohhot General Survey of Natural Resources Center, China Geological Survey, Hohhot 100024, China)

Abstract

Saline–alkaline soil is a severe threat to Sustainable Development Goals (SDGs), but it can also be a precious land resource if properly utilized according to its properties. This research takes the Songnen Plain as the study area. The aim is to figure out the saline–alkaline status and mechanisms for its scientific utilization. Sentinel-2 multispectral imagery is used, and a 3D spectral space optimization method is proposed according to the restrictive relationships among the surface soil salinity index (SSSI), vegetation index (VI), and surface soil wetness index (SSWI) to construct a surface soil salinization–alkalization index (SSSAI) for estimation of the surface soil salinity (SSS). It is testified that SSS can be precisely estimated using the SSSAI (R 2 = 0.74) with field verification of 50 surface salinized soil samples. Surface water and groundwater investigations, as well as deep soil exploration, indicate that the salt ions come from groundwater, and alkalinization is a primary problem in the deep soils. Fine-textured clay soils act as interrupted aquifers to prevent salt ions from penetrating and diluting downward with water, which is the cause of the salinization–alkalization problem in the study area. Finally, a sustainable solution for the saline–alkaline land resource is proposed according to the deep soil properties.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2069-:d:1349905
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/2069/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/2069/
    Download Restriction: no
    ---><---

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Ramos, Tiago B. & Castanheira, Nádia & Oliveira, Ana R. & Paz, Ana Marta & Darouich, Hanaa & Simionesei, Lucian & Farzamian, Mohammad & Gonçalves, Maria C., 2020. "Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal," Agricultural Water Management, Elsevier, vol. 241(C).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.

    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:gam:jsusta:v:16:y:2024:i:5:p:2069-:d:1349905. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.