黄妮等:Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County, North China
被阅读 288 次
2017-01-05
Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County, North China
作者:Huang, N (Huang, Ni)[ 1 ] ; Wang, L (Wang, Li)[ 1 ] ; Guo, YQ (Guo, Yiqiang)[ 2 ] ; Niu, Z (Niu, Zheng)[ 1 ]
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
卷: 54  页: 169-178
DOI: 10.1016/j.jag.2016.10.003
出版年: FEB 2017
 
摘要
Soil respiration (R-s) data from 45 plots were used to estimate the spatial patterns of R-s 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 R-s values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 mu mol CO2 m(-2) s(-1) and a coefficient of determination (R-2) 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 R-2, during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of R-s at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict R-s. Removal of this variable caused an RMSE increase from 0.31 mu mol CO2 m(-2) s(-1) to 0.42 mu mol 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 mu mol CO2 m(-2) s(-1) to 0.37 mu mol CO2 m(-2) s(-1). The SVR model performed better than multiple regression in predicting spatial variations of R-s in winter wheat and summer maize rotation croplands, as shown by the comparison of the R-2 and RMSE values of the two algorithms. The spatial patterns of R-s are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low R-s values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of R-s values on the basis of remotely sensed data and gridded soil property data at the county scale. (C) 2016 Elsevier B.V. All rights reserved.
 
通讯作者地址: Huang, N (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China
[ 2 ] Minist Land & Resources, Land Consolidat & Rehabil Ctr, Beijing 100035, Peoples R China