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引用本文:

DOI:

10.11834/jrs.20211323

收稿日期:

2021-05-13

修改日期:

2021-07-05

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全天候地表温度遥感获取:进展与挑战
丁利荣1, 周纪1, 张晓东2, 王韶飞1, 唐文彬3, 王子卫1, 马晋4, 艾丽皎5, 李明松4, 王伟4
1.电子科技大学 资源与环境学院;2.上海航天电子技术研究所;3.成都理工大学 地球科学学院;4.电子科技大学;5.重庆市风景园林科学研究院
摘要:

如何获取全天候地表温度对促进相关研究具有十分重要的意义。卫星热红外遥感地表温度虽然在反演理论方法和科学数据产品等方面已较为成熟,但热红外难以穿透云雾的特点导致反演得到的地表温度在云下有大量缺失;被动微波遥感虽能获取云下地表温度,但由于物理机制和成像方式的限制,存在空间分辨率不足、精度较低、有较大轨道间隙等问题。通过卫星单源遥感难以直接获取中等空间分辨率、不受云雾影响的全天候地表温度。从原理、方法、产品和应用方面回顾并归纳了当前全天候地表温度的研究进展和面临的主要问题。基于有效观测重构和多源数据集成是获取全天候地表温度的两种基本途径,前者可分为时空插值和基于能量平衡方程插值两类,后者则可分为热红外与被动微波遥感集成、热红外与再分析资料集成两类。多源数据集成可以整合热红外遥感、被动微波遥感、再分析资料各自的优势,具有较大的研究价值和潜力。在产品方面,分析了当前学术界已公开发布的五种全天候地表温度产品。文献梳理发现,目前尚未有在全球尺度上时空连续、1 km空间分辨率的全天候地表温度产品。在应用方面,虽然部分全天候地表温度产品已在土壤湿度、地表蒸散发估算与同化方面取得了一些应用成果,但其在其他领域的应用亟待挖掘。此外,对全天候地表温度的未来研究方向和重点进行了讨论和展望。

Estimation of all-weather land surface temperature with remote sensing: Progress and challenges
Abstract:

Land surface temperature (LST) is an important parameter to characterize the surface-air exchange process, which has played an important role in climate change, ecological monitoring, hydrological simulation, and other studies. Although the traditional LST estimated from thermal infrared (TIR) remote sensing has been mature in terms of retrieval methods, data production, and quality control, the TIR LST has a lot of missing data under clouds due to the limitation that TIR radiation from the ground surface is not able to penetrate the clouds. In addition, passive microwave (PMW) remote sensing also has disadvantages such as strip gaps and coarse spatial resolution due to limitations of the physical mechanisms and imaging methods. Therefore, it is very important to obtain the all-weather LST unaffected by cloudiness to support related subsequent studies. In this paper, we review and organizes the basic principles and methods of the acquisition of all-weather LST. The methods are classified into two categories: (i) all-weather LST reconstruction from effective observation; and (ii) integration of multi-source data. Through comparative analysis, we found that multi-source data integration can combine the advantages of TIR, PMW, and reanalysis data, and thus, it has the most research value and potential for further research. Multi-source data integration can be employed to obtain global long-time all-weather LST products characterized by spatial and temporal continuity. Though the LST retrieved from passive microwave remote sensing suffers from coarse spatial resolution and strip gaps, it is still an effective method of obtaining land surface information under clouds and an important input parameter for multi-source data integration. The reconstructions of all-weather LST based on effective observation are only applicable to small areas with cloud cover in short periods and are not practicable for long-term cloudy areas. In the process of analysis and conclusion, this paper also collects and analyzes information about five currently released all-weather surface temperature products, and summarizes the advantages and disadvantages of the existing products. We note that a global all-weather LST product with high quality and spatial resolution is urgently needed by the scientific community. After reviewing the all-weather LST products, we further summarized the applications of all-weather LST. We found that its application is still in infancy. Research on the application of all-weather LST is relatively small in the current stage but has great potential for application when its products further mature. Finally, further study directions and theoretical development of all-weather LST are discussed and prospected. Firstly, there are two issues that need to be addressed with PMW LST as the basis for all-weather LST: (i) filling PMW LST strip gap, so that passive microwave surface temperature has more complete spatial coverage; (ii) correcting thermal sampling depth to make that PMW LST has the same physical meaning as TIR LST. Because the inconsistent observation caused by the inconsistent thermal sampling depth is the essential reason for the inconsistent physical meaning of PMW and TIR observation information. Secondly, we should further strengthen the study on the estimation of all-weather LST from multi-source data. The current study of multi-source data integration is still in the preliminary stage and no systematic and effective integration strategy has been developed. Thirdly, the scientific community should enhance the production, publication, and application of all-weather LST products. Few all-weather LST products can be directly applied by users. The generation of all-weather LST products with global spatial and temporal continuity, as well as high spatial resolution should be the task of all-weather LST study. Besides improving the data quality and reliability of all-weather LST, we should focus on the operability and cost of the method in practical applications, so that the all-weather LST can become usable data, thus truly promoting the progress of the related studies.

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