 |
| Titel |
Remote sensing-based estimation of gross primary production in a subalpine grassland |
| VerfasserIn |
M. Rossini, S. Cogliati, M. Meroni, M. Migliavacca, M. Galvagno, L. Busetto, E. Cremonese, T. Julitta, C. Siniscalco, U. Morra di Cella, R. Colombo |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 9, no. 7 ; Nr. 9, no. 7 (2012-07-12), S.2565-2584 |
| Datensatznummer |
250007186
|
| Publikation (Nr.) |
 copernicus.org/bg-9-2565-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
This study investigates the performances in a terrestrial ecosystem of
gross primary production (GPP) estimation of a suite of spectral
vegetation indexes (VIs) that can be computed from currently orbiting
platforms. Vegetation indexes were computed from near-surface field
spectroscopy measurements collected using an automatic system designed
for high temporal frequency acquisition of spectral measurements in
the visible near-infrared region. Spectral observations were collected
for two consecutive years in Italy in a subalpine grassland equipped
with an eddy covariance (EC) flux tower that provides continuous
measurements of net ecosystem carbon dioxide (CO2) exchange (NEE)
and the derived GPP.
Different VIs were calculated based on ESA-MERIS and NASA-MODIS
spectral bands and correlated with biophysical (Leaf area index, LAI;
fraction of photosynthetically active radiation intercepted by green
vegetation, fIPARg), biochemical (chlorophyll
concentration) and ecophysiological (green light-use efficiency,
LUEg) canopy variables. In this study, the normalized difference
vegetation index (NDVI) was the index best correlated with LAI and
fIPARg (r = 0.90 and 0.95, respectively), the MERIS
terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551),
computed as (R531-R551)/(R531+R551) with LUEg
(r = 0.64).
Subsequently, these VIs were used to estimate GPP using different
modelling solutions based on Monteith's light-use efficiency model describing
the GPP as driven by the photosynthetically active radiation absorbed
by green vegetation (APARg) and by the efficiency
(ε) with which plants use the absorbed radiation to fix
carbon via photosynthesis. Results show that GPP can be successfully
modelled with a combination of VIs and meteorological data or VIs
only. Vegetation indexes designed to be more sensitive to chlorophyll
content explained most of the variability in GPP in the ecosystem
investigated, characterised by a strong seasonal dynamic of
GPP. Accuracy in GPP estimation slightly improves when taking into
account high frequency modulations of GPP driven by incident PAR or
modelling LUEg with the PRI in model formulation. Similar
results were obtained for both measured daily VIs and VIs obtained as
16-day composite time series and then downscaled from the compositing
period to daily scale (resampled data). However, the use of resampled
data rather than measured daily input data decreases the accuracy of
the total GPP estimation on an annual basis. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|