Monitoring daily cropland CO2-exchange at field scale with Sentinel-2 satellite imagery

The cropland carbon (C) balance at regional scale still contains high uncertainties not the least due to the problem of up-scaling C fluxes of temporarily and spatially highly divers ecosystems. The C-exchange between the terrestrial ecosystem and the atmosphere constitute the largest and most uncertain flux of the cropland C balance, as opposed to C import from organic (manure) and C export through harvest which are lower and less uncertain.

Combining satellite data with local eddy covariance CO₂-flux data is commonly used to up-scale the C-exchange signal from point to regional scale across global ecosystems. Low spatial resolution products like MODIS limit their applicability and accuracy to larger homogeneous areas involving a high degree of uncertainty rather than detecting and tracing highly dynamic (farm-)field scale CO₂-fluxes from space. We are using eddy-covariance CO₂-flux data of an arable field in conjunction with Sentinel-2 derived vegetation indices (VI) to assess the ability of the satellite data to monitor daily net-ecosystem exchange (NEE), gross-primary productivity (GPP) and ecosystem respiration (Reco) based on a matched footprint. Simple linear regression models are built to test the ability of a range of VIs (NDVI, GNDVI, EVI, EVI2, SAVI, MNDWI, NDWI, SR, S2REP) to monitor and predict CO₂-exchange for croplands. We analyze the correlation between measured CO₂-fluxes and VIs over the course of the growing seasons to assess the suitability and accuracy of the VIs along the phenological year. We present a single site analysis to zoom into short-comings of this approach and how the satellite signal relates to vegetation CO₂-exchange. VIs generally show a high variability in their predictive power. Still, results suggest a similarly high accuracy as mechanistic modelling approaches for suitable VIs, e.g. the cumulative C-exchange (NEE) of winter wheat of one growing season based on NDVI and GNDVI is over- and under-estimated by only 33 (15%) and 41 (18%) g C m^-2 respectively.