Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China

Abstract

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.

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 (R² and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69?g C m^?2 d^?1, respectively).

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.