Spatial variability of soil salinity in coastal saline-alkali farmlands: A novel approach integrating a stacked model with the reconstructed in-situ hyperspectral feature

文献类型： 外文期刊

第一作者： Zhan, Dexi

作者： Zhan, Dexi;Liu, Yunting;Yang, Weihao;Song, Yingqiang;Lu, Miao;Lu, Miao;Song, Yingqiang

作者机构：

关键词： Hyperspectral; Machine learning; Soil salinity; Spatial variability; Farmland

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 235 卷

页码：

收录情况： SCI

摘要： Dynamic and rapid hyperspectral monitoring of soil salinity is of great significance for the sustainable agriculture 4.0 development. However, it is difficult to retrieve the spatial variability of regional soil salinity by in-situ hyperspectral (ISH) data due to the defects of non-spatial continuity. For the end, we developed a novel ISH reconstruction method that integrates satellite-borne hyperspectral (SBH) with a trio of model learning (ML) (i.e. gradient boosting decision tree (GBDT), random forest (RF), and deep forest (DF)) into a partial least square (PLS) stacker to reconstruct the feature bands of ISH. This method enables precise predictions of the spatial variability in soil salinity across coastal saline agricultural lands. The stacker achieves a fitting accuracy (R2) of over 0.8 between SBH and ISH features in comparison with single machine learning models. Compared with ISH (R2 = 0.82 and RMSE = 1.7 g kg- 1) and SBH features (R2 = 0.39 and RMSE = 3.7 g kg- 1), the tree-structured Parzen estimator-extreme gradient boosting (TPE-XGB) model based on reconstructed in-situ hyperspectral (RISH) features achieves the highest prediction accuracy (R2 = 0.86 and RMSE = 1.0 g kg- 1) for soil salinity. The hyperparameter combination of the XGB model optimized by the TPE algorithm exhibits strong heterogeneity and high predictive performance. SHapley additive interpretation (SHAP) analysis results reveal that visible bands contribute most significantly to prediction accuracy. The spatial distribution of soil salinity concentrations in the coastal saline areas exhibits a gradient trend that decreases from the coast to the inland. Moreover, climate and vegetation factors dominate the spatial variability of soil salinity, exhibiting a synergistic effect as 85.26 % based on the variogram partitioning (VP) and hierarchical partitioning (HP) method. In summary, the stackerassisted ISH feature reconstruction method is highly feasible for predicting spatial variability of soil salinity, providing technical support for precise monitoring and intelligent management of sustainable agriculture 4.0.

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