| description abstract | Scaling up of observed point data to estimate regional carbon fluxes is an important issue in the context of the global terrestrial carbon cycle. In this study, the authors proposed a new model to scale up the eddy covariance data to estimate regional carbon fluxes using satellite-derived data. Gross primary productivity (GPP) and ecosystem respiration (RE) were empirically calculated using the normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the model input is evaluated by comparing with the field data, then established and tested the model at the point scale, and then extended it into a regional scale. At the point scale, the empirical model could reproduce the seasonal and interannual variations in the carbon budget of the mature black spruce forests in Alaska and Canada sites, suggesting that seasonality of the NDVI and LST could explain the carbon fluxes and that the model is robust within mature black spruce forests in North America. Regional-scale analysis showed that the total GPP and RE between 2003 and 2006 were 1.76 ± 0.28 and 1.86 ± 0.26 kg CO2 m?2 yr?1, respectively, in mature black spruce forests in Alaska, indicating that these forests were almost carbon neutral. The authors? model analysis shows that the proposed method is effective in scaling up point observations to estimate the regional-scale carbon budget and that the mature black spruce forests increased in sink strength during spring warming and decreased in sink strength during summer and autumn warming. | |
