Improving the Accuracy of Soil Classification by Using Vis–NIR, MIR, and Their Spectra Fusion Article Swipe
Shuo Li
,
Xinru Shen
,
Shen Xue
,
Cheng Jun
,
Dongyun Xu
,
Randa S. Makar
,
Yan Guo
,
Bifeng Hu
,
Songchao Chen
,
Yongsheng Hong
,
Jie Peng
,
Zhou Shi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17091524
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17091524
Soil spectroscopy offers a rapid, cost-effective alternative to traditional soil analyses for characterization and classification. Previous studies have mainly focused on predicting soil categories using single sensors, particularly visible–near-infrared (vis–NIR) or mid-infrared (MIR) spectroscopy. In this study, we evaluated the performance of vis–NIR, MIR, and their combined spectra for soil classification by partial least-squares discriminant analysis (PLSDA) and random forest (RF). Utilizing 60 typical soil profiles’ data of four soil classes from the global soil spectral library (GSSL), our results demonstrated that in PLSDA models, direct combination (optimal overall accuracy: 70.6%, kappa coefficient: 0.60) and outer product analysis (OPA) fused spectra (optimal overall accuracy: 68.1%, kappa coefficient: 0.57) outperformed vis–NIR (optimal overall accuracy: 62.2%, kappa coefficient: 0.49) but underperformed compared to MIR (optimal overall accuracy: 71.4%, kappa coefficient: 0.62). In RF models, classification accuracy using fused spectra was inferior to single spectral ranges, with MIR achieving the highest classification accuracy (optimal overall accuracy: 89.1%, kappa coefficient: 0.85). Therefore, MIR alone remains the most effective spectral range for accurate soil class discrimination. Our findings highlight the potential of MIR spectroscopy for enhancing global soil classification accuracy and efficiency, with important implications for soil resource management and agricultural planning across diverse environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17091524
- https://www.mdpi.com/2072-4292/17/9/1524/pdf?version=1745574572
- OA Status
- gold
- Cited By
- 1
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409813851
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- OpenAlex ID
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https://openalex.org/W4409813851Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17091524Digital Object Identifier
- Title
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Improving the Accuracy of Soil Classification by Using Vis–NIR, MIR, and Their Spectra FusionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-25Full publication date if available
- Authors
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Shuo Li, Xinru Shen, Shen Xue, Cheng Jun, Dongyun Xu, Randa S. Makar, Yan Guo, Bifeng Hu, Songchao Chen, Yongsheng Hong, Jie Peng, Zhou ShiList of authors in order
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https://doi.org/10.3390/rs17091524Publisher landing page
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https://www.mdpi.com/2072-4292/17/9/1524/pdf?version=1745574572Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/9/1524/pdf?version=1745574572Direct OA link when available
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Fusion, Remote sensing, Pattern recognition (psychology), Environmental science, Materials science, Artificial intelligence, Computer science, Geology, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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