“Take one, get two!” - Spatial flux decomposition for eddy covariance towers

Eddy covariance (EC) measurement sites are often located in heterogeneous terrain where aggregated ecosystem-exchange fluxes are observed originating from a mosaic of structured patches of different land cover types and mixed ecosystems, which may even exhibit sources and sinks simultaneously. This complex spatial heterogeneity makes it challenging to identify controls and processes governing carbon cycle processes of homogeneous sub-units surrounding the tower. As a consequence, for spatiotemporal upscaling of fluxes to large-scale maps any given tower is strictly speaking only representative for the exact same mixture of patches as found in the tower footprint.We present FLUGS, a novel framework that infers land-cover-specific ecosystem-exchange fluxes provided the EC time series of aggregated fluxes and the land cover map of the ecosystem surrounding the EC tower. Using a multitask machine learning approach based on Kernel Ridge Regression combined with high-resolution flux footprints, FLUGS learns the environmental response functions (ERFs) from EC data for each land cover class simultaneously. The approach is versatile, robust to multicollinearity and yields smooth and interpretable ERFs with a unique global optimum. By offering a fast, transparent workflow for spatially decomposing ecosystem fluxes, FLUGS opens new opportunities to attribute EC fluxes to ecological processes, benchmark land-surface models and improve our understanding of land-atmosphere interaction. In terms of data coverage, applying spatial flux decomposition with FLUGS to a single tower effectively multiplies its scientific value, providing land-cover-specific insights equivalent to operating two or more conventional towers, one for each patch type individually. The FLUGS framework is validated against synthetic and real data experiments. The latter uses data from a twin tower site in Northeast Siberia and the STORDALENX25 campaign. Machine learned patch-level ERFs from FLUGS may be used directly for upscaling.