Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan, Canada
作者:Wu, CY (Wu, Chaoyang)[ 1,2 ] ; Gaumont-Guay, D (Gaumont-Guay, David)[ 3 ] ; Black, TA (Black, T. Andrew)[ 4 ] ; Jassal, RS (Jassal, Rachhpal S.)[ 4 ] ; Xu, SG (Xu, Shiguang)[ 1 ] ; Chen, JM (Chen, Jing M.)[ 2 ] ; Gonsamo, A (Gonsamo, Alemu)[ 2 ]
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷: 94  页: 80-90
DOI: 10.1016/j.isprsjprs.2014.04.018
出版年: AUG 2014

摘要
Soil respiration (R-s) is of great importance to the global carbon balance. Remote sensing of R-s is challenging because of (1) the lack of long-term R-s data for model development and (2) limited knowledge of using satellite-based products to estimate R-s. Using 8-years (2002-2009) of continuous R-s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R-s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R-s and NDVI x LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R-s closely tracked the seasonal patterns of measured R-s and explained 74-92% of the variance in R-s with a root mean square error (RMSE) less than 1.0 g C/m(2)/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R-s with an overall R-2 of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R-s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R-s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R-s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R-s. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

通讯作者地址: Wu, CY (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Univ Toronto, Dept Geog, Toronto, ON M5S 1A1, Canada
[ 3 ] Vancouver Isl Univ, Biometeorol Res Lab, Nanaimo, BC, Canada
[ 4 ] Univ British Columbia, Fac Land & Food Syst, Vancouver, BC V5Z 1M9, Canada