Study on the Extraction of Topsoil-Loss Areas of Cultivated Land Based on Multi-Source Remote Sensing Data Article Swipe
Xinle Zhang
,
Chuan Qin
,
S. Ma
,
Jiming Liu
,
Yiang Wang
,
Huanjun Liu
,
Zeyu An
,
Yuanye Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17030547
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17030547
Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17030547
- OA Status
- gold
- Cited By
- 4
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407223772
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407223772Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17030547Digital Object Identifier
- Title
-
Study on the Extraction of Topsoil-Loss Areas of Cultivated Land Based on Multi-Source Remote Sensing DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-06Full publication date if available
- Authors
-
Xinle Zhang, Chuan Qin, S. Ma, Jiming Liu, Yiang Wang, Huanjun Liu, Zeyu An, Yuanye MaList of authors in order
- Landing page
-
https://doi.org/10.3390/rs17030547Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/rs17030547Direct OA link when available
- Concepts
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Topsoil, Extraction (chemistry), Remote sensing, Environmental science, Cultivated land, Soil science, Land use, Geology, Soil water, Civil engineering, Chemistry, Engineering, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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66Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.topsoil-loss | 51 |
| abstract_inverted_index.classification | 249 |
| abstract_inverted_index.identification | 182 |
| abstract_inverted_index.socio-economic | 45 |
| abstract_inverted_index.sustainability | 43 |
| abstract_inverted_index.time-consuming | 69 |
| abstract_inverted_index.labor-intensive | 71 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.97597126 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |