A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas
文献类型：期刊论文
| 作者 | Wang, Ju2,3,4; Tang, Bo-Hui1,2,3,4; Zhu, Xinming2,3,4; Fan, Dong2,3,4; Li, Menghua2,3,4; Chen, Junyi2,3,4 |
| 刊名 | FRONTIERS IN EARTH SCIENCE
 |
| 出版日期 | 2025-01-09 |
| 卷号 | 12页码:1488711 |
| 关键词 | land surface temperature downscaling Landsat-9 machine learning XGBoost |
| DOI | 10.3389/feart.2024.1488711 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Land surface temperature (LST) is a crucial factor for reflecting climate change. High spatial resolution LST is particularly significant for environmental monitoring in plateau and mountainous areas, which are characterized by rugged landscapes, diverse ecosystems, and high spatial variability in LST. Typical plateau mountainous areas in Diqing Tibetan Autonomous Prefecture and Dali Bai Autonomous Prefecture were selected as study areas. Three machine learning models, including Back Propagation (BP) Neural Network, random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. Particle Swarm Optimization (PSO) was introduced to optimize hyperparameters of three machine learning methods. Regression factors suitable for plateau mountainous areas, including normalized vegetation index (NDVI), normalized multi-band drought index (NMDI), bare soil index (BSI), normalized difference snow index (NDSI), elevation, surface roughness (SR), and Hillshade were selected. The performance of five models was analyzed from the perspective of different spatial resolutions and land cover types. The results revealed that the performance of machine learning models is better than traditional linear models in both study areas. Based on the coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. For study area A, the results were R 2 = 0.891, RMSE = 2.67 K, and MAE = 1.83 K, while for study area B, the values were R 2 = 0.832, RMSE = 1.98 K, and MAE = 1.54 K. In addition, among different land cover types, the XGBoost model has the best performance in both study areas. Moreover, the larger the ratio of initial resolution to target resolution, the lower the accuracy of downscaled LST (DLST). In summary, the XGBoost model is more suitable for downscaling LST in plateau mountainous areas. |
| URL标识 | 查看原文 |
| WOS关键词 | ENERGY FLUXES ; DISAGGREGATION ; ALGORITHMS ; SCALE |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001402601600001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211335]  |
| 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 | Tang, Bo-Hui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Yunnan Int Joint Lab Integrated Sky Ground Intelli, Kunming, Peoples R China; 3.Yunnan Key Lab Quantitat Remote Sensing, Kunming, Peoples R China; 4.Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Ju,Tang, Bo-Hui,Zhu, Xinming,et al. A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas[J]. FRONTIERS IN EARTH SCIENCE,2025,12:1488711. |
| APA | Wang, Ju,Tang, Bo-Hui,Zhu, Xinming,Fan, Dong,Li, Menghua,&Chen, Junyi.(2025).A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas.FRONTIERS IN EARTH SCIENCE,12,1488711. |
| MLA | Wang, Ju,et al."A comparative analysis of five land surface temperature downscaling methods in plateau mountainous areas".FRONTIERS IN EARTH SCIENCE 12(2025):1488711. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。