文献类型： 外文期刊
作者： Zhu, Jin 1 ; Yang, Shuowen 1 ; Li, Shuyan 1 ; Zhou, Nan 1 ; Shen, Yi 2 ; Xing, Jincheng 3 ; Xu, Lixin 1 ; Hong, Zhichao 1 ; Yang, Yifei 1 ;
作者机构： 1.Jiangsu Univ Sci & Technol, Ocean Coll, Zhenjiang 212003, Peoples R China
2.Jiangsu Acad Agr Sci, Inst Ind Crops, Nanjing 210000, Peoples R China
3.Inst Agr Sci, Salt Soil Agr Res Lab Jiangsu Coastal Area, Yancheng 224000, Peoples R China
4.Jiangsu Marine Technol Innovat Ctr, Nantong 226000, Peoples R China
关键词： tidal flat agriculture; remote sensing inversion; spectral indices; precision breeding
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 3 期
页码：
收录情况： SCI
摘要： 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 (R2 = 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.
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