Estimating soil respiration using spatial data products: A case study in a deciduous broadleaf forest in the Midwest USA
作者:Huang, N (Huang, Ni)[ 1 ] ; Gu, LH (Gu, Lianhong)[ 2 ] ; Niu, Z (Niu, Zheng)[ 1 ]
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
卷: 119  期: 11  页: 6393-6408
DOI: 10.1002/2013JD020515
出版年: JUN 16 2014

摘要
This study aimed to investigate the potential of spatially distributed data products in estimating soil respiration (Rs), including land surface temperature (LST) and spectral vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) and root zone soil moisture derived from the assimilation of the NASA Advanced Microwave Scanning Radiometer-EOS and a land surface model, at a deciduous broadleaf forest site in the Midwest USA. Several statistical models were used to examine the dependencies of Rs on these spatial data products, and accuracy of these models was compared to the models based on in situ measurements. The models based on mean LST (i.e., averaging nighttime and daytime LST from MODIS) and root zone soil moisture explained 82% and 72% of seasonal variations in Rs for spring and winter dormant periods, respectively. In the growing season, the models depending on mean LST, root zone soil moisture, and photosynthesis-related enhanced vegetation index showed comparable accuracy with the models entirely based on in situ measured data, except for the midgrowing period. Drought stress led to a relatively low explanation capacity for the Rs model based on spatial data products during the midgrowing period. However, this model still explained 76% of temporal dynamics of Rs over the midgrowing period. Our results suggested that simple models based entirely on spatial data products have the potential to estimate Rs at the temperate deciduous forest site. The conclusions drawn from the present study provided valuable information for large-scale estimates of Rs in temperate deciduous forest ecosystems.

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, 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 ] Oak Ridge Natl Lab, Div Environm Sci, Oak Ridge, TN 37831 USA