Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data Article Swipe
Meiyan Xiang
,
Qing Rao
,
Xiaohang Yang
,
Xiaoqian Wu
,
Dexi Zhan
,
Jin Zhang
,
Miao Lu
,
Yingqiang Song
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi14100403
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi14100403
Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R2 = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R2 = 0.65), alkali-hydrolyzable nitrogen (R2 = 0.56), and available potassium (R2 = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi14100403
- https://www.mdpi.com/2220-9964/14/10/403/pdf?version=1760525698
- OA Status
- gold
- References
- 67
- OpenAlex ID
- https://openalex.org/W4415230688
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https://openalex.org/W4415230688Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/ijgi14100403Digital Object Identifier
- Title
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Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral DataWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-15Full publication date if available
- Authors
-
Meiyan Xiang, Qing Rao, Xiaohang Yang, Xiaoqian Wu, Dexi Zhan, Jin Zhang, Miao Lu, Yingqiang SongList of authors in order
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https://doi.org/10.3390/ijgi14100403Publisher landing page
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https://www.mdpi.com/2220-9964/14/10/403/pdf?version=1760525698Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2220-9964/14/10/403/pdf?version=1760525698Direct OA link when available
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0Total citation count in OpenAlex
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67Number of works referenced by this work
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| corresponding_author_ids | https://openalex.org/A5070779777 |
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