Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression

Carbon dioxide (CO2) fluxes from a network of 21 eddy covariance towers were upscaled to estimate the Alaskan CO2 budget from 2000 to 2011 by combining satellite remote sensing data, disturbance information, and a support vector regression model. Data were compared with the CO2 budget from an inverse model (CarbonTracker). Observed gross primary productivity (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE) were each well reproduced by the model on the site scale; root-mean-square errors (RMSEs) for GPP, RE, and NEE were 0.52, 0.23, and 0.48g C m(-2) d(-1), respectively. Landcover classification was the most important input for predicting GPP, whereas visible reflectance index of green ratio was the most important input for predicting RE. During the period of 2000-2011, predicted GPP and RE were 36922 and 36212 Tg C yr(-1) (meaninterannual variability) for Alaska, respectively, indicating an approximately neutral CO2 budget for the decade. CarbonTracker also showed an approximately neutral CO2 budget during 2000-2011 (growing season RMSE=14g C m(-2) season(-1); annual RMSE=13g C m(-2) yr(-1)). Interannual CO2 flux variability was positively correlated with air temperature anomalies from June to August, with Alaska acting as a greater CO2 sink in warmer years. CO2 flux trends for the decade were clear in disturbed ecosystems; positive trends in GPP and CO2 sink were observed in areas where vegetation recovered for about 20 years after fire.