Data from: Spatial variation of soil respiration in a cropland under winter wheat and summer maize rotation in the North China Plain

Spatial variation of soil respiration (Rs) in cropland ecosystems must be
assessed to evaluate the global terrestrial carbon budget. This study aims
to explore the spatial characteristics and controlling factors of Rs in a
cropland under winter wheat and summer maize rotation in the North China
Plain. We collected Rs data from 23 sample plots in the cropland. At the
late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2
s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2
s-1). However, the spatial variation of Rs in summer maize (coefficient of
variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A
similar trend in CV was also observed for environmental factors but not
for biotic factors, such as leaf area index, aboveground biomass, and
canopy chlorophyll content. Pearson’s correlation analyses based on the
sampling data revealed that the spatial variation of Rs was poorly
explained by the spatial variations of biotic factors, environmental
factors, or soil properties alone for winter wheat and summer maize. The
similarly non-significant relationship was observed between Rs and the
enhanced vegetation index (EVI), which was used as surrogate for plant
photosynthesis. EVI was better correlated with field-measured leaf area
index than the normalized difference vegetation index and red edge
chlorophyll index. All the data from the 23 sample plots were categorized
into three clusters based on the cluster analysis of soil carbon/nitrogen
and soil organic carbon content. An apparent improvement was observed in
the relationship between Rs and EVI in each cluster for both winter wheat
and summer maize. The spatial variation of Rs in the cropland under winter
wheat and summer maize rotation could be attributed to the differences in
spatial variations of soil properties and biotic factors. The results
indicate that applying cluster analysis to minimize differences in soil
properties among different clusters can improve the role of remote sensing
data as a proxy of plant photosynthesis in semi-empirical Rs models and
benefit the acquisition of Rs in cropland ecosystems at large scales.