A general method for downscaling earth resource information

A programme scripted for use in an R programming environment called dissever is presented. This programme was designed to facilitate a generalised method for downscaling coarsely resolved earth resource information using available finely gridded covariate data. Under the assumption that the relationship between the target variable being downscaled and the available covariates can be nonlinear, dissever uses weighted generalised additive models (GAMs) to drive the empirical function. An iterative algorithm of GAM fitting and adjustment attempts to optimise the downscaling to ensure that the target variable value given for each coarse grid cell equals the average of all target variable values at the fine scale in each coarse grid cell. A number of outputs needed for mapping results and diagnostic purposes are automatically generated from dissever. We demonstrate the programs' functionality by downscaling a soil organic carbon (SOC) map with 1-km by 1-km grid resolution down to a 90-m by 90-m grid resolution using available covariate information derived from a digital elevation model, Landsat ETM+ data, and airborne gamma radiometric data. dissever produced high quality results as indicated by a low weighted root mean square error between averaged 90-m SOC predictions within their corresponding 1-km grid cell (0.82 kg m−3). Additionally, from a concordance between the downscaled map and another map created using digital soil mapping methods there was a strong agreement (0.94). Future versioning of dissever will investigate quantifying the uncertainty of the downscaled outputs.