Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system

To examine the method for estimating the spatial patterns of soil
respiration (Rs) in agricultural ecosystems using remote sensing and
geographical information system (GIS), Rs rates were measured at 53 sites
during the peak growing season of maize in three counties in North China.
Through Pearson's correlation analysis, leaf area index (LAI), canopy
chlorophyll content, aboveground biomass, soil organic carbon (SOC)
content, and soil total nitrogen content were selected as the factors that
affected spatial variability in Rs during the peak growing season of
maize. The use of a structural equation modeling approach revealed that
only LAI and SOC content directly affected Rs. Meanwhile, other factors
indirectly affected Rs through LAI and SOC content. When three greenness
vegetation indices were extracted from an optical image of an
environmental and disaster mitigation satellite in China, enhanced
vegetation index (EVI) showed the best correlation with LAI and was thus
used as a proxy for LAI to estimate Rs at the regional scale. The spatial
distribution of SOC content was obtained by extrapolating the SOC content
at the plot scale based on the kriging interpolation method in GIS. When
data were pooled for 38 plots, a first-order exponential analysis
indicated that approximately 73% of the spatial variability in Rs during
the peak growing season of maize can be explained by EVI and SOC content.
Further test analysis based on independent data from 15 plots showed that
the simple exponential model had acceptable accuracy in estimating the
spatial patterns of Rs in maize fields on the basis of remotely sensed EVI
and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square
error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide
valuable information for estimates of Rs during the peak growing season of
maize in three counties in North China.