Volume 45 Issue 2
Apr.  2025
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Article Navigation >  Chinese Journal of Space Science  > 2025  >  45(2): 413-423 Open Access
LI Jingyang, HE Jieying. Progress and Prospects of the Combined Application of Microwave Remote Sensing and Infrared Hyperspectral Remote Sensing (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 413-423 doi: 10.11728/cjss2025.02.2024-0195
Citation: LI Jingyang, HE Jieying. Progress and Prospects of the Combined Application of Microwave Remote Sensing and Infrared Hyperspectral Remote Sensing (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 413-423 doi: 10.11728/cjss2025.02.2024-0195
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Progress and Prospects of the Combined Application of Microwave Remote Sensing and Infrared Hyperspectral Remote Sensing

doi: 10.11728/cjss2025.02.2024-0195 cstr: 32142.14.cjss.2024-0195

  • Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • Received Date: 2024-12-24
  • Accepted Date: 2025-03-17
  • Rev Recd Date: 2025-03-10
    • Available Online: 2025-04-21
    Abstract
  • With the advancement of Earth science research, single remote sensing technologies face limitations in meeting the demands for accuracy, spatiotemporal resolution, and data dimensions in complex Earth system observations. This study reviews the combined application of microwave and infrared hyperspectral remote sensing. Through literature analysis, the fundamental principles and characteristics of both technologies are explored, their applications in disaster management and ecological environment monitoring are summarized, and the progress and challenges of multi-sensor data fusion are examined. Challenges include spatiotemporal resolution mismatches, multi-sensor calibration, and data processing complexities. The findings demonstrate that integrating microwave and infrared hyperspectral remote sensing can improve observation accuracy and data coverage, supporting weather forecasting, disaster response, and ecological protection. Future work should focus on optimizing fusion algorithms and data processing techniques to transition from theoretical research to practical applications, providing robust tools for global climate change and environmental studies.

     

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