 |
| Titel |
Effect of spatial heterogeneity on remotely sensed GPP |
| VerfasserIn |
Natascha Kljun, Györgyi Gelybó, Zoltán Barcza, Anikó Kern |
| Konferenz |
EGU General Assembly 2013
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250079207
|
|
|
|
|
|
| Zusammenfassung |
| Satellite based remote sensing provides an efficient way to estimate carbon balance
components over large spatial domains with acceptable temporal and spatial resolution.
However, for heterogeneous landscapes these remotely sensed data may be biased towards
one dominant land-cover type. In the present study, remote-sensing based gross primary
production estimates (GPP MOD17, 1 km x 1 km spatial resolution) were evaluated using
data from a tall eddy-covariance flux tower located over a heterogeneous agricultural
landscape in Hungary. We present a novel approach for GPP model validation, exploiting the
advantage of footprint-size similarity between remote sensing and the hourly eddy covariance
signal measured at the tall tower. Further, we present a new methodology for improved
remote-sensing based GPP estimates. This methodology addresses land-use heterogeneity
by incorporating a footprint climatology and by downscaling MOD17 GPP using
the 250-m resolution MODIS-NDVI (Normalized Difference Vegetation Index)
dataset.
The results show that GPP was underestimated by MOD17 especially in years with
average precipitation during the growing season, while model performance was better during
dry years. Our downscaling technique significantly improved agreement between the
MOD17 model results and the eddy-covariance measurements (modelling efficiency
(ME) increased from 0.783 to 0.884, root mean square error (RMSE) decreased
from 1.095 g C m-2 day-1 to 0.815 g C m-2 day-1), although GPP remained
underestimated (bias decreased from -0.680 g C m-2 day-1 to -0.426 g C m-2
day-1).
The presented methods are applicable to any eddy-covariance tower with limitations
depending on the complexity of landscape around the flux tower. As incorporation of
footprint information clearly impacts validation results, future model validation and/or
calibration should also involve source area estimation which can be easily implemented
following the presented approach. |
|
|
|
|
|
|