The carbon budget of the managed grasslands of Great Britain – informed by earth observations

Grasslands cover around two-thirds of the agricultural land area of Great Britain (GB) and are important reservoirs of organic carbon (C). Direct assessments of the C balance of grasslands require continuous monitoring of C pools and fluxes, which is only possible at a small number of experimental sites. By relying on our quantitative understanding of ecosystem C biogeochemistry we develop models of grassland C dynamics and use them to estimate grassland C balance at various scales. Model-based estimation of the C budget of individual fields and across large domains is made complex by the spatial and temporal variability in climate and soil conditions, as well as in livestock grazing, grass cutting and other management activities. In this context, earth observations (EOs) provide subfield-resolution proxy data on the state of grassland canopies, allowing us to infer information about vegetation management, to apply observational constraints to the simulated ecosystems and, thus, to mitigate the effects of model input data uncertainty. Here, we show the potential of model–data fusion (MDF) methods to provide robust analyses of C dynamics in managed grasslands across GB. We combine EO data and biogeochemical modelling by implementing a probabilistic MDF algorithm to (1) assimilate leaf area index (LAI) times series (Sentinel-2); (2) infer defoliation instances (grazing, cutting); and (3) simulate livestock grazing, grass cutting, and C allocation and C exchanges with the atmosphere. The algorithm uses the inferred information on grazing and cutting to drive the model's C removals-and-returns module, according to which ≈1/3 of C in grazed biomass returns to the soil as manure (other inputs of manure not considered) and C in cut grass is removed from the system (downstream C emissions not considered). Spatial information on soil C stocks is obtained from the SoilGrids dataset. The MDF algorithm was applied for 2017–2018 to generate probabilistic estimates of C pools and fluxes at 1855 fields sampled from across GB. The algorithm was able to effectively assimilate the Sentinel-2-based LAI time series (overlap = 80 %, RMSE = 1.1 m2 m−2, bias = 0.35 m2 m−2) and predict livestock densities per area that correspond with independent agricultural census-based data (r = 0.68, RMSE = 0.45 LU ha−1, bias = −0.06 LU ha−1). The mean total removed biomass across all simulated fields was 6 (±1.8) t DM ha−1 yr−1. The simulated grassland ecosystems were on average C sinks in 2