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 (R(s)) 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 R(s) in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected R(s) data from 23 sample plots in the cropland. At the late jointing stage, the daily mean R(s) of summer maize (4.74 μmol CO(2) m(-2) s(-1)) was significantly higher than that of winter wheat (3.77μmol CO(2) m(-2) s(-1)). However, the spatial variation of R(s) 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 R(s) 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 R(s) 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 R(s) and EVI in each cluster for both winter wheat and summer maize. The spatial variation of R(s) 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 R(s) models and benefit the acquisition of R(s) in cropland ecosystems at large scales.