DOI: 10.1016/j.jag.2016.10.003
Scopus记录号: 2-s2.0-85018651405
论文题名: Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County, North China
作者: Huang N ; , Wang L ; , Guo Y ; , Niu Z
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 54 起始页码: 169
结束页码: 178
语种: 英语
英文关键词: Landsat 8
; Soil property
; Soil respiration
; Support vector regression
; Winter wheat and summer maize rotation
Scopus关键词: agricultural land
; crop rotation
; Landsat
; maize
; remote sensing
; soil property
; soil respiration
; support vector machine
; wheat
; China
; Julu
; Triticum aestivum
; Zea mays
英文摘要: Soil respiration (Rs) data from 45 plots were used to estimate the spatial patterns of Rs during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed Rs values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO2 m−2 s−1 and a coefficient of determination (R2) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of Rs during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of Rs at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict Rs. Removal of this variable caused an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.42 μmol CO2 m−2 s−1. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.37 μmol CO2 m−2 s−1. The SVR model performed better than multiple regression in predicting spatial variations of Rs in winter wheat and summer maize rotation croplands, as shown by the comparison of the R2 and RMSE values of the two algorithms. The spatial patterns of Rs are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low Rs values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of Rs values on the basis of remotely sensed data and gridded soil property data at the county scale. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79946
Appears in Collections: 气候变化事实与影响
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作者单位: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; Land Consolidation and Rehabilitation Center, Ministry of Land and Resources, Beijing, China
Recommended Citation:
Huang N,, Wang L,, Guo Y,et al. Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County, North China[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,54