Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing
Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing
复制标题
用于遥感空间降尺度的地理加权面对点回归克里金法
DOI：
10.3390/rs10040579
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发表时间：
2018-04-01
期刊：
影响因子：
5
通讯作者：
Wang, Le
中科院分区：
文献类型：
--
作者：
Jin, Yan;Ge, Yong;Wang, Le
遥感产品的空间降尺度是获取高分辨率对地观测数据的主要途径之一。将遥感产品的规则细网格视为点的面到点（ATP）地统计学技术已被广泛应用于空间降尺度。在空间降尺度中，通常使用辅助信息来解释目标地理变量的一些未知空间变化。由于普遍存在的空间异质性，观测变量总是表现出不可控的方差。针对ATP回归克里格法中局部异质性导致的空间尺度不稳定问题，提出了一种结合地理加权回归和ATP克里格法的混合空间统计方法。建议的地理加权ATP回归克里金（GWATTORY）结合了精细的空间分辨率辅助信息，并允许在降尺度模型的非平稳性。该方法进行了验证，使用8组4个不同的25公里分辨率的表层土壤水分（SSM）遥感产品，以获得1公里的SSM预测在两个实验区，结合实施的三个基准方法。不同尺度下的结果的分析和比较表明，GWATTALGOs获得了更好的质量和平均损失与均方根误差值为17.5%的尺度下的精细空间分辨率图像。分析表明，所提出的方法具有很高的潜力，在遥感应用的空间降尺度。
Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications.