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Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model

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  • Jin Zhu

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    These authors contributed equally to this work.)

  • Shuowen Yang

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    These authors contributed equally to this work.)

  • Shuyan Li

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

  • Nan Zhou

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

  • Yi Shen

    (The Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210000, China)

  • Jincheng Xing

    (The Salt Soil Agriculture Research Laboratory at Jiangsu Coastal Area Institute of Agricultural Sciences, Yancheng 224000, China)

  • Lixin Xu

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    Jiangsu Marine Technology Innovation Center, Nantong 226000, China)

  • Zhichao Hong

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
    Jiangsu Marine Technology Innovation Center, Nantong 226000, China)

  • Yifei Yang

    (Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

Abstract

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R 2 = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions.

Suggested Citation

  • Jin Zhu & Shuowen Yang & Shuyan Li & Nan Zhou & Yi Shen & Jincheng Xing & Lixin Xu & Zhichao Hong & Yifei Yang, 2025. "Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model," Agriculture, MDPI, vol. 15(3), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:323-:d:1581947
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    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.
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