The GRRIEn Analysis Framework: effective supervised learning of earth surface processes at large spatial scales.

Globally available, georeferenced data from earth observing satellites and coupled earth systems models provide new opportunities to use limited field observations to infer trends in environmental processes across unsampled locations and over time. Building statistical models that are intended generalize earth surface processes across large spatial scales represents a new frontier in supervised statistical inference. For example, care must be taken to collect unbiased samples given the complexity of the earth system: surface features can appear vastly different from the perspective of multispectral and SAR imagery in different atmospheric/landscape contexts. Environmental processes occur at variable spatial/temporal scales, and sampling resolution can drastically alter the appearance of patterns, and lead to spatial and temporal autocorrelation which can bias model weights and/or parameter estimates. Multicollinearity in multivariate datasets, which is also scale dependent, can inflate variance in parameter/weight estimates. All these in tandem can undermine the robustness of models in predicting in out-of-sample contexts. To overcome this, the GRRIEn (Generalizable, Reproducible, Robust, and Interpretable Environmental) analysis framework is introduced as a standard method of training and validating supervised data-driven models at large spatial scales. The method is explained, and demonstrated with a case study detecting surface water at CONUS scale using SAR and multispectral imagery.