A framework for reconstructing 1km all-weather hourly LST from MODIS data
文献类型：期刊论文
| 作者 | Yan, Jianan; Ni, Li; Li, Xiujuan; Cheng, Yuanliang; Wu, Hua |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2023-08-11 |
| 卷号 | N/A页码:2242591 |
| ISSN号 | 0143-1161 |
| 关键词 | All weather annual temperature cycle land surface temperature |
| DOI | 10.1080/01431161.2023.2242591 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K similar to 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 similar to 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness. |
| WOS关键词 | LAND-SURFACE TEMPERATURE ; WATER |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001044943000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194520]  |
| 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.Institute of Geographic Sciences & Natural Resources Research, CAS 2.University of Chinese Academy of Sciences, CAS 3.Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Yan, Jianan,Ni, Li,Li, Xiujuan,et al. A framework for reconstructing 1km all-weather hourly LST from MODIS data[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2023,N/A:2242591. |
| APA | Yan, Jianan,Ni, Li,Li, Xiujuan,Cheng, Yuanliang,&Wu, Hua.(2023).A framework for reconstructing 1km all-weather hourly LST from MODIS data.INTERNATIONAL JOURNAL OF REMOTE SENSING,N/A,2242591. |
| MLA | Yan, Jianan,et al."A framework for reconstructing 1km all-weather hourly LST from MODIS data".INTERNATIONAL JOURNAL OF REMOTE SENSING N/A(2023):2242591. |
入库方式： OAI收割
来源：地理科学与资源研究所
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