Tang, XG; Zhou, YL; Li, HP; Yao, L; Ding, Z; Ma, MG; Yu, PJ (2020). Remotely monitoring ecosystem respiration from various grasslands along a large-scale east-west transect across northern China. CARBON BALANCE AND MANAGEMENT, 15(1), 6.

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 CO2 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.