Spatial Downscaling of Remote Sensing Parameters from Perspective of Data Fusion
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 applica-tion capabilities.Researchers have developed various downscaling methods for different remote sensing pa-rameters.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 sum-marizes the spatial downscaling methods from the perspective of data fusion,taking into account the re-quired 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 tech-nical support for improving the refined application ability of remote sensing data.
NETL
NSTL
万方数据
