2022
DOI: 10.1080/15481603.2022.2143807
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Mapping Australia’s precipitation: harnessing the synergies of multi-satellite remote sensing and gauge network data

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Cited by 5 publications
(2 citation statements)
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“…Regression kriging was adopted for spatial interpolation modeling, which predicts unknown areas by combining a regression model with auxiliary variables and then adding the kriging prediction of residuals (Hengl et al ., 2007; Hines et al ., 2022; Wang et al ., 2012). Regression kriging has been demonstrated to have a higher interpolation accuracy than traditional spatial interpolation techniques (e.g., inverse distance weighting and kriging) and pure regression (Fayad et al ., 2016; Fox et al ., 2020; Li et al ., 2011).…”
Section: Methodsmentioning
confidence: 99%
“…Regression kriging was adopted for spatial interpolation modeling, which predicts unknown areas by combining a regression model with auxiliary variables and then adding the kriging prediction of residuals (Hengl et al ., 2007; Hines et al ., 2022; Wang et al ., 2012). Regression kriging has been demonstrated to have a higher interpolation accuracy than traditional spatial interpolation techniques (e.g., inverse distance weighting and kriging) and pure regression (Fayad et al ., 2016; Fox et al ., 2020; Li et al ., 2011).…”
Section: Methodsmentioning
confidence: 99%
“… 43 We included four different single algorithms: support vector regression (SVR), multi-linear regression (MLR), local linear forest (LLF), and cokriging. 44 , 45 , 46 Cokriging is a geostatistical approach based on spatial autocorrelation that combines one or more covariates to predict the target value of an unsampled location using known samples. For cokriging in this study, DEM was chosen as a covariable because elevation is closely associated with air temperature.…”
Section: Methodsmentioning
confidence: 99%