2.3. Data processing
After data collection, we performed quality control on all flux and
meteorological data. Since small deviations can be ignored with low
canopy height in flat study sites, axial rotation and trend elimination
for raw eddy data were not performed (Zhao et al. 2006, Fu et al. 2009).
We use the data processing method of flux from ChinaFLUX (Yu et al.
2008). The data quality was improved by spike screening and night-time
filtering. The spike was defined as the beyond 4.5 standard deviations
of a 10-day moving window. The night-time flux data collected under low
atmospheric turbulence conditions were screened using thresholds of
friction velocity (u*), and flux data were rejected when
u* < 0.15 m·s−1. Then, the
missing CO2 flux data were filled by empirical
non-linear regression of valid data with environmental variables (Ma et
al., 2007; Li et al., 2016). Daily GPP was obtained by subtracting NEE
from RES (Eq. (1)), and daily RES was the sum of nocturnal respiration
(RESn) and daytime respiration (RESd),
which was extrapolated the exponential regressions of
RESn with nighttime soil temperature to the daytime
periods. Negative and positive NEE represent the ecosystem to absorb and
release CO2 (Yu et al. 2008).
GPP = RES − NEE = (RESd + RESn) − NEE
(1)