 |
| Titel |
A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling |
| VerfasserIn |
B. Chen, Q. Ge, D. Fu, G. Yu, X. Sun, S. Wang, H. Wang |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 7, no. 9 ; Nr. 7, no. 9 (2010-09-27), S.2943-2958 |
| Datensatznummer |
250004977
|
| Publikation (Nr.) |
 copernicus.org/bg-7-2943-2010.pdf |
|
|
|
|
|
| Zusammenfassung |
| In order to use the global available eddy-covariance (EC) flux
dataset and remote-sensing measurements to provide estimates of
gross primary productivity (GPP) at landscape
(101–102 km2), regional
(103–106 km2) and global land surface scales, we
developed a satellite-based GPP algorithm using LANDSAT data and an
upscaling framework. The satellite-based GPP algorithm uses two
improved vegetation indices (Enhanced Vegetation Index – EVI, Land
Surface Water Index – LSWI). The upscalling framework involves flux
footprint climatology modelling and data-model fusion. This approach
was first applied to an evergreen coniferous stand in the
subtropical monsoon climatic zone of south China. The EC
measurements at Qian Yan Zhou tower site (26°44´48" N,
115°04´13" E), which belongs to the China flux network
and the LANDSAT and MODIS imagery data for this region in 2004 were
used in this study. A consecutive series of LANDSAT-like images of
the surface reflectance at an 8-day interval were predicted by
blending the LANDSAT and MODIS images using an existing algorithm
(ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion
Model). The seasonal dynamics of GPP were then predicted by the
satellite-based algorithm. MODIS products explained 60% of observed
variations of GPP and underestimated the measured annual GPP
(= 1879 g C m−2) by 25–30%; while the satellite-based
algorithm with default static parameters explained 88% of observed
variations of GPP but overestimated GPP during the growing seasonal
by about 20–25%. The optimization of the satellite-based algorithm
using a data-model fusion technique with the assistance of EC flux
tower footprint modelling reduced the biases in daily GPP
estimations from about 2.24 g C m−2 day−1
(non-optimized, ~43.5% of mean measured daily value) to
1.18 g C m−2 day−1 (optimized, ~22.9% of mean
measured daily value). The remotely sensed GPP using the optimized
algorithm can explain 92% of the seasonal variations of EC observed
GPP. These results demonstrated the potential combination of the
satellite-based algorithm, flux footprint modelling and data-model
fusion for improving the accuracy of landscape/regional GPP
estimation, a key component for the study of the carbon cycle. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|