数据融合视角下的遥感参量空间降尺度

Spatial Downscaling of Remote Sensing Parameters from Perspective of Data Fusion

空间分辨率不足是限制遥感参量数据精细应用的主要瓶颈问题之一,而空间降尺度是提升遥感参量数据空间分辨率与应用能力的有效途径。研究学者针对不同遥感参量已发展了类型多样的降尺度方法,但还未形成统一、通用的分类体系。在深入分析当前各类参量降尺度共性问题的基础上,从降尺度所需的互补信息出发,以数据融合的视角对空间降尺度方法进行系统总结,归纳出多参量融合、时-空融合、遥感-模型融合和超分辨率重建隐式融合4类降尺度方法,剖析了各类方法的优缺点和适用场景,探讨了空间降尺度方法研究的发展趋势,为提升遥感数据精细化应用能力提供理论与技术支撑。

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 sum-marizes 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.