Machine Learning-Based Comparative Analysis on Direct and Indirect Mapping of Soil Texture Types Through Soil Particle Size Fractions Using Multi-Source Remote Sensing Article Swipe
Jia Liu
,
Yingcong Ye
,
Cui Wang
,
Songchao Chen
,
Yameng Jiang
,
Guo Xi
,
Yefeng Jiang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15131395
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15131395
Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models.
Related Topics
Concepts
Soil texture
Texture (cosmology)
Particle size
Soil classification
Particle (ecology)
Environmental science
Biological system
Soil science
Pattern recognition (psychology)
Remote sensing
Computer science
Mathematics
Artificial intelligence
Soil water
Geology
Biology
Ecology
Engineering
Image (mathematics)
Chemical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15131395
- https://www.mdpi.com/2077-0472/15/13/1395/pdf?version=1751104801
- OA Status
- gold
- Cited By
- 2
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411832798
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411832798Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/agriculture15131395Digital Object Identifier
- Title
-
Machine Learning-Based Comparative Analysis on Direct and Indirect Mapping of Soil Texture Types Through Soil Particle Size Fractions Using Multi-Source Remote SensingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-28Full publication date if available
- Authors
-
Jia Liu, Yingcong Ye, Cui Wang, Songchao Chen, Yameng Jiang, Guo Xi, Yefeng JiangList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture15131395Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0472/15/13/1395/pdf?version=1751104801Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-0472/15/13/1395/pdf?version=1751104801Direct OA link when available
- Concepts
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Soil texture, Texture (cosmology), Particle size, Soil classification, Particle (ecology), Environmental science, Biological system, Soil science, Pattern recognition (psychology), Remote sensing, Computer science, Mathematics, Artificial intelligence, Soil water, Geology, Biology, Ecology, Engineering, Image (mathematics), Chemical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
-
82Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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