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

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

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...

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Bibliographic Details
Main Authors: Jin Zhu, Shuowen Yang, Shuyan Li, Nan Zhou, Yi Shen, Jincheng Xing, Lixin Xu, Zhichao Hong, Yifei Yang
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Agriculture
Subjects:
tidal flat agriculture
remote sensing inversion
spectral indices
precision breeding
Online Access:https://www.mdpi.com/2077-0472/15/3/323
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Summary: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<sup>2</sup> = 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.
ISSN:2077-0472

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