The annual dynamics of carbon dioxide (CO2) fluxes for irrigated and rainfed alfalfa (Medicago sativa L.) in the Southern Great Plains of the United States of America (USA) under different watering regimes are not yet fully understood. The main objective of this study was to examine the dynamics of eddy covariance (EC) measured CO2 fluxes in relation to various biophysical factors and hay harvests for irrigated and rainfed alfalfa in central Oklahoma, USA. The study also aimed to investigate the relationship between CO2 fluxes and satellite-derived enhanced vegetation index (EVI) at different spatiotemporal scales and to assess the temporal variability in CO2 fluxes and EVI to variable growing conditions and hay harvests. The cumulative hay yields were 7.15 t ha−1 (two harvests in 2019) in the rainfed field and ∼9 t ha−1 (4–5 harvests in 2020 and 2021) in the irrigated field. Having sufficient rainfall during April and May was crucial to achieve economically feasible yields of alfalfa during the first harvest in May. The availability of water strongly regulated the potential for regrowth and carbon uptake of alfalfa following harvesting. The alfalfa fields were near carbon neutral or a small carbon source from January to mid-March and carbon sink after the initiation of vegetative growth in mid-March. The alfalfa fields were strong carbon sinks (cumulative annual net ecosystem CO2 exchange, NEE, up to −578 g C m−2 in irrigated field) on an annual scale. When accounting for the loss of carbon due to the removal of hay from the fields, the carbon balance of the alfalfa fields varied from small carbon sinks to small carbon sources, depending on the amount of hay harvested annually and the growing conditions. In general, the temporal patterns of CO2 fluxes and EVI were similar in relation to growing conditions and hay harvests. However, some discrepancies and time lags were observed due to the coarse spatiotemporal resolution of the EVI products. Thus, it is essential to integrate two or more satellite products with different temporal and spatial resolutions to accurately monitor the frequent and varying sizes of hay harvests and vegetation regrowth after harvesting, and to simulate continuous time-series CO2 fluxes.
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