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JING Yinghong, SHEN Huanfeng, LI Xinghua, WU Jingan, QIU Zhonghang. Spatial Downscaling of Remote Sensing Parameters from Perspective of Data Fusion[J]. Geomatics and Information Science of Wuhan University, 2024, 49(2): 175-189. DOI: 10.13203/j.whugis20220549
Citation: JING Yinghong, SHEN Huanfeng, LI Xinghua, WU Jingan, QIU Zhonghang. Spatial Downscaling of Remote Sensing Parameters from Perspective of Data Fusion[J]. Geomatics and Information Science of Wuhan University, 2024, 49(2): 175-189. DOI: 10.13203/j.whugis20220549
shu

Spatial Downscaling of Remote Sensing Parameters from Perspective of Data Fusion

  • 1.

    School of Resource and Environmental Sciences, Wuhan University, Wuhan430079, China

  • 2.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan430079, China

  • 3.

    School of Geospatial Engineering and Science, Sun Yat‐Sen University, Zhuhai519082, China

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  • Received Date: September 01, 2022
  • Available Online: August 15, 2023
    Graphical Abstract
    • Abstract

  • Low spatial resolution is one of the main bottlenecks restricting the fine application of remote sensing parameters. Spatial downscaling is an effective way to improve their spatial resolution and application capabilities. Researchers have developed various downscaling methods for different remote sensing parameters. However, a unified and general method classification system has not yet been formed. Based on the in-depth commonality analysis of the downscaling of various parameters, this paper systematically summarizes the spatial downscaling methods from the perspective of data fusion, taking into account the required complementary information. Four types of downscaling methods are summarized: Multi-parameter fusion, spatiotemporal fusion, remote sensing-model fusion, and super-resolution reconstruction implicit fusion. The advantag‍es, disadvantages, and applicable scenarios of various methods are analyzed, and the development trend of spatial downscaling method research is discussed. It can provide theoretical and technical support for improving the refined application ability of remote sensing data.

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