黄妮等：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
来源：发布时间：2014-10-20
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
作者:Huang, N (Huang, Ni)[ 1 ] ; Wang, L (Wang, Li)[ 1 ] ; Guo, YQ (Guo, Yiqiang)[ 2 ] ; Hao, PY (Hao, Pengyu)[ 1 ] ; Niu, Z (Niu, Zheng)[ 1 ]
PLOS ONE
卷: 9 期: 8
文献号: e105150
DOI: 10.1371/journal.pone.0105150
出版年: AUG 26 2014
摘要
To examine the method for estimating the spatial patterns of soil respiration (R-s) in agricultural ecosystems using remote sensing and geographical information system (GIS), R-s 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 R-s during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected R-s. Meanwhile, other factors indirectly affected R-s 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 R-s 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 R-s 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 R-s in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R-2 of 0.69 and root-mean-square error of 0.51 mu mol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of R-s during the peak growing season of maize in three counties in North China.
通讯作者地址: Wang, L (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Land Consolidat & Rehabil Ctr, Beijing, Peoples R China
作者:Huang, N (Huang, Ni)[ 1 ] ; Wang, L (Wang, Li)[ 1 ] ; Guo, YQ (Guo, Yiqiang)[ 2 ] ; Hao, PY (Hao, Pengyu)[ 1 ] ; Niu, Z (Niu, Zheng)[ 1 ]
PLOS ONE
卷: 9 期: 8
文献号: e105150
DOI: 10.1371/journal.pone.0105150
出版年: AUG 26 2014
摘要
To examine the method for estimating the spatial patterns of soil respiration (R-s) in agricultural ecosystems using remote sensing and geographical information system (GIS), R-s 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 R-s during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected R-s. Meanwhile, other factors indirectly affected R-s 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 R-s 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 R-s 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 R-s in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R-2 of 0.69 and root-mean-square error of 0.51 mu mol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of R-s during the peak growing season of maize in three counties in North China.
通讯作者地址: Wang, L (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Land Consolidat & Rehabil Ctr, Beijing, Peoples R China
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