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Abstract

Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (>0.9) and statistically significant (p<0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 %) and an overestimation of gross C uptake of 854 g C m−2 (73 %). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.