Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information
文献类型:期刊论文
作者 | Song, Peilin; Li, Xiaojie4; Ma, Zonghan3; Wu, Shengli2 |
刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION |
出版日期 | 2024-04-01 |
卷号 | 128页码:103703 |
关键词 | Land surface temperature (LST) Passive microwave Thermal infrared (TIR) Cloud -shadowing bias Cloud total amount |
DOI | 10.1016/j.jag.2024.103703 |
产权排序 | 2 |
文献子类 | Article |
英文摘要 | Fusion of thermal infrared (TIR) and passive microwave (PMW) observations are important techniques for estimating all-weather land surface temperature (LST) of relatively higher resolutions. For such fusion techniques under cloudy weather, PMW observations are especially useful in correcting the cloud -shadowing bias of the real cloudy LST against a theoretically clear -sky one in the daytime. However, PMW observations have very limited transit times in a day, and cannot be fused with asynchronous TIR data as LST can vary significantly during the daytime. To overcome this flaw, we developed a model for simulating the variation of cloud -shadowing bias based on cloud total amount (CTA) information observed by geostationary satellites. As constant coefficients of the model were found to have dependence on space and time, they were dynamically fitted for each pixel in each month using cloud -shadowing bias calculated by TIR and synchronous PMW data in the early -afternoon (1:30 P. M.). The model was then used to provide middle -morning (10:30 A.M.) cloud -shadowing bias estimates, based on which, all-weather LST at 1 km were produced at that specific moment. Validation results show that reconstructed cloudy LST in the middle -morning, with no synchronous PMW observations, has its overall RMSE values against ground measurements and real remote sensing data under cloud between 2.4 and 4.0 K. This accuracy result is generally comparable to that obtained through fusing information from synchronous PMW data in the early -afternoon moment. Therefore, it is prospective to be further applied for generating high -resolution allweather LST with much shorter re -visit cycles than the satellite PMW data. |
WOS关键词 | MICROWAVE ; CHINA ; CYCLE ; ALGORITHM ; FRAMEWORK ; PRODUCTS ; DATASET ; MODEL |
WOS研究方向 | Remote Sensing |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:001188355000001 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/203338] |
专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
作者单位 | 1.China Meteorol Adm, Natl Satellite Meteorol Ctr, Fengyun Meteorol Satellite Innovat Ctr, Beijing 100081, Peoples R China 2.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Xi An Jiao Tong Univ, Key Lab Phys Elect & Devices, Minist Educ, Xian 710049, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Song, Peilin,Li, Xiaojie,Ma, Zonghan,et al. Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,128:103703. |
APA | Song, Peilin,Li, Xiaojie,Ma, Zonghan,&Wu, Shengli.(2024).Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,128,103703. |
MLA | Song, Peilin,et al."Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 128(2024):103703. |
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。