Local adjustments of image spatial resolution to optimize large-area mapping in the era of big data

Abstract

Sentinel-2 has opened a new era for the remote sensing community where 10-m imagery is freely available with a 5-day revisit frequency and a systematic global coverage. Having both frequent and detailed observations across large geographic areas are ideal characteristics that can potentially revolutionize applications such as crop mapping and monitoring. However, such large volumes of high-resolution data pose challenges to users in terms of problem complexity, computational resources and processing time, beckoning the increasingly relevant question: at which resolution should this imagery be processed? Here, we develop a methodology to characterize resolution-dependent errors in cropland mapping and explore their behavior when we move across spatial scales and landscapes, taking special care to include the effects of the instrument's Point Spread Function (PSF). Results show how local upscaling of 10-m imagery, e.g., from Sentinel-2, to 30 m mitigates most the adverse effects generated by the PSF when comparing it to native 30-m imagery, e.g., from Landsat-8. Extending this logic, we demonstrate for two nationwide cases how maps can be calculated showing the optimal spatial resolution that keeps resolution-dependent errors below a user-defined threshold. Based on these maps, we estimate that 31% of Belgium and 59% of South Africa could be processed at 20 m instead of 10 m, while keeping the increase of resolution-dependent errors below 3%. These local resolution adjustments lead to a reduction in data volume and processing time by 23% and 44%, respectively.