Interpretable machine learning and uncertainty quantification for high-precision fractional vegetation cover inversion across scales in alpine grasslands
文献类型：期刊论文
| 作者 | Chen, Jianjun8; Li, Xinhong3,8; Yi, Shuhua1; Wang, Zhiwei3; Chen, Zizhen5; Qin, Yu4; Huang, Qinyi8; Li, Hucheng1,8; Han, Xiaowen8; You, Haotian8 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2026-04-01 |
| 卷号 | 148页码:105212 |
| 关键词 | Fractional Vegetation Cover Transfer Learning Inversion Uncertainty Quantifying SHapley Additive exPlanations |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2026.105212 |
| 产权排序 | 8 |
| 文献子类 | Article |
| 英文摘要 | Fractional Vegetation Cover (FVC) is a critical indicator for grassland monitoring and ecological assessment, playing a vital role in advancing ecological sustainability research. However, high-resolution FVC inversion faces two persistent challenges: (1) unquantified uncertainty arising from spatial misalignment between UAV measurement footprints and satellite imagery pixels (SMUS), and (2) heavy reliance on concurrent in-situ measurements and satellite imagery acquisitions, which limits robust mapping capabilities across scales and regions. To address these gaps, this study develops a multi-source remote sensing framework that integrates three key components. First, we introduced PSO-SHAP, an interpretable feature selection algorithm that integrates Particle Swarm Optimization (PSO) and SHapley Additive exPlanations (SHAP). Second, we established an uncertainty quantification framework using a 5%-interval overlap gradient to quantify SMUS-induced inversion uncertainty. Finally, we constructed a cross-scale transfer learning scheme for high-accuracy mapping and elucidated the feature transfer mechanisms. The results demonstrated that: (1) PSO-SHAP effectively balanced feature importance and interpretability; (2) misalignment-induced uncertainty decreased nonlinearly with increasing overlap degree, stabilizing beyond approximately 85% overlap, with maximum uncertainties of 27.88% for Landsat 8/9 and 7.74% for Sentinel-2A/B; and (3) machine learning models exhibited cross-scale transferability, while SHAP analysis interpreted the transfer processes and revealed the scale-invariant advantages of key features. Additionally, Sentinel-2A/B outperformed Landsat 8/9 in 30-m FVC mapping accuracy and spatial continuity. The proposed framework presents a novel methodology for multi-source synergistic FVC inversion and provides a reference for inverting and validating diverse ecological parameters. |
| URL标识 | 查看原文 |
| WOS关键词 | TIBETAN PLATEAU ; MODIS ; MAP |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001710115200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221208]  |
| 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 | Yi, Shuhua; Zhou, Guoqing |
| 作者单位 | 1.Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Herbage Improvement & Grassland Agro, Lanzhou 730000, Peoples R China; 2.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing & Digital Earth, Beijing 100875, Peoples R China; 3.Guizhou Acad Agr Sci, Guizhou Inst Prataculture, Guiyang 550006, Guizhou, Peoples R China; 4.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci & Frozen Soil Engn, Lanzhou 730000, Peoples R China; 5.Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Hubei, Peoples R China; 6.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing, Peoples R China; 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 8.Guilin Univ Technol, Coll Geomat & Geoinformat, Guangxi Key Lab Spatial Informat & Geomat, Guilin 541004, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Jianjun,Li, Xinhong,Yi, Shuhua,et al. Interpretable machine learning and uncertainty quantification for high-precision fractional vegetation cover inversion across scales in alpine grasslands[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,148:105212. |
| APA | Chen, Jianjun.,Li, Xinhong.,Yi, Shuhua.,Wang, Zhiwei.,Chen, Zizhen.,...&Zhou, Guoqing.(2026).Interpretable machine learning and uncertainty quantification for high-precision fractional vegetation cover inversion across scales in alpine grasslands.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,148,105212. |
| MLA | Chen, Jianjun,et al."Interpretable machine learning and uncertainty quantification for high-precision fractional vegetation cover inversion across scales in alpine grasslands".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 148(2026):105212. |
入库方式： OAI收割
来源：地理科学与资源研究所
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