IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i12p2307-d1004866.html
   My bibliography  Save this article

Estimation and Dynamic Analysis of Soil Salinity Based on UAV and Sentinel-2A Multispectral Imagery in the Coastal Area, China

Author

Listed:
  • Zixuan Zhang

    (School of Geographical Sciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Beibei Niu

    (School of Resources and Environment, Shandong Agricultural University, Taian 271018, China)

  • Xinju Li

    (School of Resources and Environment, Shandong Agricultural University, Taian 271018, China)

  • Xingjian Kang

    (School of Geographical Sciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Zhenqi Hu

    (School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R 2 reaches 0.769, verification accuracy R 2 reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2307-:d:1004866
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/12/2307/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/12/2307/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zare, Ehsan & Arshad, Maryam & Zhao, Dongxue & Nachimuthu, Gunasekhar & Triantafilis, John, 2020. "Two-dimensional time-lapse imaging of soil wetting and drying cycle using EM38 data across a flood irrigation cotton field," Agricultural Water Management, Elsevier, vol. 241(C).
    2. 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.
    3. Lingling Bian & Juanle Wang & Jing Liu & Baomin Han, 2021. "Spatiotemporal Changes of Soil Salinization in the Yellow River Delta of China from 2015 to 2019," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    4. Jing Liu & Li Zhang & Tong Dong & Juanle Wang & Yanmin Fan & Hongqi Wu & Qinglong Geng & Qiangjun Yang & Zhibin Zhang, 2021. "The Applicability of Remote Sensing Models of Soil Salinization Based on Feature Space," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    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. 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.
    3. Jiawen Hou & Yusufujiang Rusuli, 2022. "Assessment of Soil Salinization Risk by Remote Sensing-Based Ecological Index (RSEI) in the Bosten Lake Watershed, Xinjiang in Northwest China," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Mei Xu & Bing Guo & Rui Zhang, 2024. "A Novel Approach to Detecting the Salinization of the Yellow River Delta Using a Kernel Normalized Difference Vegetation Index and a Feature Space Model," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. repec:caa:jnlswr:v:preprint:id:5-2024-swr is not listed on IDEAS
    17. Jun Zhang & Lin Li, 2022. "Spatial and Temporal Characteristics of Infiltration Wetting Front of Ring-Shaped Root Emitters," Sustainability, MDPI, vol. 14(11), pages 1-14, May.
    18. 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.
    19. 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.
    20. 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.
    21. 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.

    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:jlands:v:11:y:2022:i:12:p:2307-:d:1004866. 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.