title: 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.
abstract: 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.
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Ni
authors.initials: N
authors.lastname: Huang
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Land Consolidation and Rehabilitation Center, Ministry of Land and Resources, Beijing, China.
authors.forename: Yiqiang
authors.initials: Y
authors.lastname: Guo
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Pengyu
authors.initials: P
authors.lastname: Hao
authors.affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Niu
mesh_terms: D018533:Biomass; D002244:Carbon / Q000032:analysis; D002245:Carbon Dioxide / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods; D040362:Geographic Information Systems; D008954:Models, Biological; D008956:Models, Chemical; D018515:Plant Leaves / Q000254:growth & development; D058998:Remote Sensing Technology / Q000379:methods; D012621:Seasons; D012987:Soil / Q000737:chemistry*; D003313:Zea mays / Q000254:growth & development*
chemical_list: D012987:Soil; D002245:Carbon Dioxide; D002244:Carbon