Abstract
BACKGROUND: Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (R(e)) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given R(e) occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of R(e) compared to GPP. RESULTS: Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by R(e) as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean R(e) revealed relatively less CO(2) emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in R(e), which implied the great potential to derive R(e) using relevant remote sensing data. Then, these field-measured R(e) data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R(2) and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m(-2) d(-1), respectively). CONCLUSIONS: Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean R(e) observations and the remotely-derived products were usually within 20%. Finally, the regional R(e) emissions across northern China's grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of R(e) over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.