Enhanced Spatially Explicit Modeling of Soil Particle Size and Texture Classification Using a Novel Two-Point Machine Learning Hybrid Framework
文献类型：期刊论文
| 作者 | Qin, Liya1; Wang, Zong1,3; Zhang, Xiaoyuan2 |
| 刊名 | AGRICULTURE-BASEL
 |
| 出版日期 | 2025-09-25 |
| 卷号 | 15期号:19页码:2008 |
| 关键词 | digital soil mapping soil particle size fractions machine learning soil texture two-point machine learning model |
| DOI | 10.3390/agriculture15192008 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R2 values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63-98.28% for silt, 0.26-36.16% for clay, and 0.64-96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. |
| URL标识 | 查看原文 |
| WOS关键词 | HEIHE RIVER-BASIN ; ORGANIC-CARBON ; FRACTIONS ; REGRESSION ; TREE |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001593383200001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217541]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Zong |
| 作者单位 | 1.Beijing Forestry Univ, Coll Forestry, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China; 2.Beijing Technol & Business Univ, Business Sch, Beijing 100048, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Qin, Liya,Wang, Zong,Zhang, Xiaoyuan. Enhanced Spatially Explicit Modeling of Soil Particle Size and Texture Classification Using a Novel Two-Point Machine Learning Hybrid Framework[J]. AGRICULTURE-BASEL,2025,15(19):2008. |
| APA | Qin, Liya,Wang, Zong,&Zhang, Xiaoyuan.(2025).Enhanced Spatially Explicit Modeling of Soil Particle Size and Texture Classification Using a Novel Two-Point Machine Learning Hybrid Framework.AGRICULTURE-BASEL,15(19),2008. |
| MLA | Qin, Liya,et al."Enhanced Spatially Explicit Modeling of Soil Particle Size and Texture Classification Using a Novel Two-Point Machine Learning Hybrid Framework".AGRICULTURE-BASEL 15.19(2025):2008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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