A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data
文献类型：期刊论文
| 作者 | Li, Xiujuan4; Wu, Hua4,6; Ni, Li4,5; Li, Jing3; Zhang, Xingxing4; Fan, Dong2; Cheng, Yuanliang4 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 134页码:104170 |
| 关键词 | Land surface emissivity retrieval Machine learning Thermal infrared Visible and near infrared Short-wave infrared |
| DOI | 10.1016/j.jag.2024.104170 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Land surface emissivity (LSE) is a crucial variable in thermal infrared (TIR) remote sensing, providing unique information about the land surface across different channels. It is essential for applications such as surface energy budget estimation, resource exploration, and land cover change monitoring. However, current methods for retrieving LSE have certain limitations in terms of applicability or accuracy levels. Furthermore, the relative importance of various parameters in LSE retrieval studies remains unclear. To address these challenges, a practical and transferrable method has been proposed to retrieve LSE of different TIR channels using machine-learning technique. The proposed method uses visible and near-infrared (VNIR) as well as short-wave infrared (SWIR) data at the pixel scale to analyze key parameters for LSE retrieval and to estimate LSE for channels centered around 8.6 mu m, 11.0 mu m and 12.0 mu m. Importance analysis identified crucial variables for LSE retrieval, including reflectivity in channels of SWIR3 (similar to 2.13 mu m), RED (similar to 0.66 mu m) and BLUE (similar to 0.47 mu m), as well as the Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI) and the view zenith angle (VZ). Compared to the data used in existing methods, the core variables offer a more comprehensive representation of surface information, potentially enhancing both the accuracy and usability of the proposed method. Using these core variables, LSE was retrieved across eleven study areas through a machine learning method. Cross-validation with MODIS products showed that the Root Mean Square Error (RMSE) of the estimated LSE is 0.02 for the channel around 8.6 mu m, and 0.01 for the channels around 11.0 mu m and 12.0 mu m, respectively. Direct-validation with in-situ measurements also demonstrated impressive retrieval accuracies in sandy areas. Furthermore, the model trained using 2019 data exhibited high retrieval accuracy when applied to data from 2017, highlighting its transferability across different time periods. Additionally, the proposed method produced promising results for LSE estimation using Landsat 8 imageries, indicating its potential for generating emissivity products from satellites with high spatial resolution but limited TIR channels. |
| WOS关键词 | BROAD-BAND EMISSIVITY ; FIELD VALIDATION ; TEMPERATURE ; MODIS ; ALGORITHM ; SEPARATION ; INDEX ; VEGETATION ; MIDDLE ; AREAS |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001318191000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207939]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Kunming Univ Sci & Technol, Kunming 650093, Yunnan, Peoples R China 2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 5.Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 610054, Sichuan, Peoples R China 6.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 | Li, Xiujuan,Wu, Hua,Ni, Li,et al. A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,134:104170. |
| APA | Li, Xiujuan.,Wu, Hua.,Ni, Li.,Li, Jing.,Zhang, Xingxing.,...&Cheng, Yuanliang.(2024).A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,134,104170. |
| MLA | Li, Xiujuan,et al."A practical machine learning approach to retrieve land surface emissivity from space using visible and near-infrared to short-wave infrared data".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 134(2024):104170. |
入库方式： OAI收割
来源：地理科学与资源研究所
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