Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing

Abstract

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.

Downscaling satellite soil moisture using geomorphometry and machine learning

“…These include models estimating water evapotranspiration trends [86] and process based global carbon models that could also benefit from more accurate and independent soil moisture inputs [78]. To improve the spatial representativeness of satellite soil moisture estimates, the number of studies developing new downscaling approaches based on prediction factors is rapidly expanding [26, 28, 66, 87]. There is a pressing need to solve the current uncertainty of soil moisture estimates to accurately understand how soil moisture is limiting the primary productivity of terrestrial ecosystems [6].…”

Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model

“…In addition, time-series data with narrower time gaps can improve the correlation. Moreover, with the development of the technology, there may be other interpolation methods worth trying, such as the area-to-point kiriging (ATPK) interpolation with considering the spatial autocorrelation [64], [65]. Finally, not only MODIS LST temperature products can be downscaled but also other diurnal LST products can be downscaled.…”

Downscaling Census Data for Gridded Population Mapping With Geographically Weighted Area-to-Point Regression Kriging

“…Due to the lacking of population data at the fine grid scale, the population prediction of fine grid cells in (1) is impossible [45]. To benefit from detailed information of covariates at the fine grid scale, the invariable assumption of regression coefficients at different scales can be taken in GWR model [45], [47] like the existing gridded population mapping using random forest regression [1], [31] and classification and regression tree [25]. Thus, the coefficients of β 0 (•) and β k (•) in (2) can be replaced with the coefficients of GWR calculated at the irregular census unit scale, that is…”