سابِق|تال

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

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：dlryouxiang@163.com
  • Introduction：丁利荣，研究方向为全天候地表温度反演与深度学习。E-mail： dlryouxiang@163.com
DING Lirong1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

    The Yangtze Delta Region Institute (Huzhou),University of Electronic Science and Technology of China, Huzhou 313001, China

  • Email：jzhou233@uestc.edu.cn
  • Introduction：周纪，研究方向为定量遥感。E-mail： jzhou233@uestc.edu.cn
ZHOU Ji12*,  
  • Affiliation：

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

    Shanghai Spaceflight Institute of TT and C and Telecommunication, Shanghai 201109, China

ZHANG Xiaodong34,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Shaofei1,  
  • Affiliation：

    School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

TANG Wenbin5,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Ziwei1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

MA Jin1,  
  • Affiliation：

    Chongqing Landscape and Gardening Research Institute, Chongqing 401329

AI Lijiao6,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

LI Mingsong1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

WANG Wei1

ملخص

Land Surface Temperature (LST) is an important parameter for characterizing the surface–air exchange process, which plays an important role in climate change, ecological monitoring, hydrological simulation, and other studies. The traditional LST estimated from Thermal Infrared (TIR) remote sensing is mature in terms of retrieval methods, data production, and quality control. However, the TIR LST has considerable missing data under clouds because of the limitation that the TIR radiation from the ground surface cannot penetrate the clouds. In addition, Passive Microwave (PMW) remote sensing has disadvantages, such as strip gaps and coarse spatial resolution, because of the limitations of the physical mechanisms and imaging methods. Therefore, the all-weather LST unaffected by cloudiness must be obtained to support the subsequent studies. In the present study, we review and organize 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) multisource data integration.The comparative analysis indicates that multisource data integration can combine the advantages of TIR, PMW, and reanalysis data. Thus, it has the highest research value and potential for further research. Multisource data integration can be employed to obtain global long-time all-weather LST products characterized by spatial and temporal continuity. The LST retrieved from PMW remote sensing suffers from coarse spatial resolution and strip gaps. However, it is still an effective method of obtaining land surface information under clouds and an important input parameter for multisource data integration. The reconstructions of all-weather LST based on effective observation only apply to small areas with cloud cover in short periods. They are not practicable for long-term cloudy areas.From the analysis and conclusion, this study also collects and analyzes information about five currently released all-weather surface temperature products. The advantages and disadvantages of the existing products are also summarized. 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 summarize the applications of all-weather LST. Its applications are still in their infancy. Research on the applications of all-weather LST is relatively small in the current stage. However, all-weather LST has great potential for applications when its products further mature.Finally, further study directions and theoretical development of all-weather LST are discussed and prospected. First, with PMW LST as the basis for all-weather LST, two issues must be addressed: (i) filling the PMW LST strip gap to make the PMW surface temperature a complete spatial coverage; (ii) correcting thermal sampling depth to make that PMW LST obtain the same physical meaning as TIR LST. The reason is that the inconsistent observation caused by the varying thermal sampling depth is the actual reason for the inconsistent physical meaning of PMW and TIR observation information. Second, we should further strengthen the study on estimating all-weather LST from multisource data. The current study of multisource data integration is still in the preliminary stage, and no systematic and effective integration strategy has been developed. Third, 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. Generating all-weather LST products with global spatial and temporal continuity and high spatial resolution should be the task of an all-weather LST study. Besides improving the data quality and reliability of all-weather LST, focusing on the operability and cost of the method in practical applications is necessary to make the all-weather LST usable data, thereby truly promoting the progress of the related studies.

مفهوم

remote sensing;all-weather land surface temperature;reconstruction;interpolation;multi-source data integration

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