Scientists yet to consider spatial correlation in assessing uncertainty of spatial averages and totals

Abstract

High-resolution maps of climate and ecosystem variables are essential for supporting terrestrial carbon stocks and fluxes estimation, climate change mitigation, and ecosystem degradation assessment. These maps are usually created using remotely sensed data obtained from various types of imagery and sensors. The remote sensing data typically serve as covariates to deliver spatially explicit information using machine learning algorithms. Often the uncertainty associated with the maps is also quantified, for instance by prediction error variance maps or by maps of the lower and upper limits of a prediction interval. In addition, these products are often aggregated to regional, national, or global scales relevant to climate policy, natural resource inventory, and measurement, reporting, and verification (MRV) frameworks. Quantifying uncertainty in aggregated products is crucial as it is necessary to assess their value and evaluate whether changes and trends in aggregated estimates are statistically significant. However, we argue that such uncertainty is frequently inaccurately assessed due to the neglect of spatial correlation in map errors. This critical methodological issue has been overlooked in most large-scale mapping studies.