 |
| Titel |
Assessing the seasonality of multi-source fAPAR time series |
| VerfasserIn |
Carola Weiss, Alexander Loew, Christian Reick |
| Konferenz |
EGU General Assembly 2011
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250057025
|
|
|
|
|
|
| Zusammenfassung |
| The fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is an essential
diagnostic variable to investigate the temporal and spatial dynamics of the terrestrial
biosphere. We introduce a new method to compare global vegetation greening phase
dynamics, derived from fAPAR time series from remote sensing sensors, and from climate
models.
This poster provides a two-tier presentation of this study: First, the greening phase pattern
analysis is presented. The method is developed to compare the phase of seasonal signals in
multi-annual data sets. We apply it to three long term satellite vegetation data sets, namely
from AVHRR (Advanced Very High Resoluted Radiometer), SeaWiFS (Sea-viewing Wide
Field-of-view), and MODIS (Moderate Resolution Imaging Spectroradiometer)
sensors.
While previous comparison studies focused on the estimation of specific phenological
events, e.g. spring date, the current study introduces a new algorithm that allows for the
robust identification of seasonal signals from multi-sensor time series with a special focus on
the difference in the seasonal phases of the characteristic signal. By vector normalisation and
integral calculation, we isolate the seasonal signal from any amplitudinal aspects that are
influenced by the inconsistencies of the various data sets. The comparison of three
independent remote sensing data sets shows significantly consistent global spatio-temporal
patterns at the 95% confidence level. Based on the monthly resolved data sets that have
been evaluated in this study, no remarkable shifts are visible. Shifts stay in the
range of +/- 1 month, which is the expected minimum shift for monthly resolved
data.
Second, the robust greening phase pattern algorithm is ideal for the assessment of
seasonal processes simulated by the vegetation components of climate models. A couple of
models have been evaluated by the greening phase pattern analysis. Regions where the
seasonality of the models agree with the remote sensing data sets can be detected as well as
regions, where models and observations disagree. Results from global as well as regional
evaluations are presented. |
|
|
|
|
|
|