Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data

Worldwide, cropland ecosystems play a significant role in the global carbon (C) cycle. However, quantifying and understanding the cropland C cycle are complex, due to variable environmental drivers, varied management practices and often highly heterogeneous landscapes. Efforts to upscale processes using simulation models must resolve these challenges. In this study we show how data assimilation (DA) approaches can link C cycle modelling to Earth observation (EO) and reduce uncertainty in upscaling. We evaluate a framework for the assimilation of leaf area index (LAI) time-series, derived from EO optical and radar sensors, for state-updating a model of crop development and C fluxes. Sensors are selected with fine spatial resolutions (20-50 m) to resolve variability across field sizes typically used in European agriculture (1.5-97.6 ha). Sequential DA is used to improve the canopy development simulation, which is validated by comparing time-series of net ecosystem exchange (NEE) predictions to independent eddy covariance observations at multiple European cereal crop sites. From assimilating all EO LAI estimates, results indicated adjustments in LAI and, through an enhanced representation of C exchanges, the predicted at-harvest cumulative NEE was improved for all sites by an average of 69% when compared to the model without DA. However, using radar sensors, being relatively unaffected by cloud cover and more sensitive to the structural properties of crops, further improvements were achieved when compared to the combined, and individual, use of optical data. Specifically, when assimilating radar LAI estimates only, the cumulative NEE estimation was improved by 79% when compared to the simulation without DA. Future developments would include the assimilation of additional state variables, such as soil moisture.