Machine learning land surface temperature downscaling method based on Landsat 9 and Sentinel-2 satellite feature interaction
文献类型：期刊论文
| 作者 | Yuan, Wenrui1,5; Hu, Shi5; Zhan, Chesheng2; Wang, Guoqiang3; Luo, Youyu1,4 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2025-12-15 |
| 卷号 | N/A |
| 关键词 | Land surface temperature downscaling machine learning Landsat 9 Sentinel-2 SHAP |
| ISSN号 | 1009-5020 |
| DOI | 10.1080/10095020.2025.2598526 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | As a core essential climate variable (ECV) in the Global Climate Observing System, land surface temperature (LST) plays a pivotal role in climate change monitoring, urban thermal environment assessment, agricultural management, and ecosystem surveillance. To obtain high-precision LST data, a novel machine learning-based LST downscaling framework integrating feature interaction optimization and Shapley additive explanations (SHAP) scoring was proposed. SHAP scoring was employed for feature importance analysis to identify optimal predictors, while 10 distinct models were comparatively evaluated to establish a high-resolution downscaling framework adaptable to homogeneous surface characteristics. The results show that the SHAP-based feature selection significantly enhanced prediction accuracy by prioritizing nonlinear determinants. The red-blue band interaction feature demonstrated consistent dominance across all algorithms (XGBoost, LightGBM, GradientBoost), exhibiting both the broadest SHAP value range (-2.0 to 2.0) and the highest relative contribution weight. By explicitly addressing spatial heterogeneity, the spatial random forest (SRF) model achieved superior downscaling performance, particularly in vegetated regions. It generated reliable 10 m-resolution LST estimates (R2 = 0.74, RMSE = 6.28 degrees C), demonstrating robust generalization capabilities in complex terrain conditions. The SHAP-based land surface temperature downscaling method can effectively capture the nonlinear interactions among spectral, topographic, and other features, demonstrating high accuracy and strong physical interpretability in high-resolution temperature retrieval over areas dominated by homogeneous vegetation. |
| URL标识 | 查看原文 |
| WOS关键词 | SPECTRAL REFLECTANCE ; SOIL SURFACE ; MOISTURE ; RETRIEVAL ; ETM+ ; AREA ; GIS |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001639563400001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219526]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Hu, Shi |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Earth Syst Numer Modeling & Applicat, Beijing, Peoples R China; 3.Beijing Normal Univ, Adv Interdisciplinary Inst Satellite Applicat, Beijing, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yuan, Wenrui,Hu, Shi,Zhan, Chesheng,et al. Machine learning land surface temperature downscaling method based on Landsat 9 and Sentinel-2 satellite feature interaction[J]. GEO-SPATIAL INFORMATION SCIENCE,2025,N/A. |
| APA | Yuan, Wenrui,Hu, Shi,Zhan, Chesheng,Wang, Guoqiang,&Luo, Youyu.(2025).Machine learning land surface temperature downscaling method based on Landsat 9 and Sentinel-2 satellite feature interaction.GEO-SPATIAL INFORMATION SCIENCE,N/A. |
| MLA | Yuan, Wenrui,et al."Machine learning land surface temperature downscaling method based on Landsat 9 and Sentinel-2 satellite feature interaction".GEO-SPATIAL INFORMATION SCIENCE N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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