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| Titel |
Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site |
| VerfasserIn |
G. Mendiguren, M. Pilar Martín, H. Nieto, J. Pacheco-Labrador, S. Jurdao |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 12, no. 18 ; Nr. 12, no. 18 (2015-09-29), S.5523-5535 |
| Datensatznummer |
250118103
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| Publikation (Nr.) |
 copernicus.org/bg-12-5523-2015.pdf |
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| Zusammenfassung |
This study evaluates three different metrics of water content of an
herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel
moisture content (FMC), equivalent water thickness (EWT) and canopy water
content (CWC) were estimated from proximal sensing and MODIS satellite
imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics
and were also analyzed. Metrics were derived from field sampling of grass
cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and
MODIS images were simultaneously acquired and predictive empirical models
were parametrized. Two methods of estimating FMC and CWC using different
field protocols were tested in order to evaluate the consistency of the
metrics and the relationships with the predictive empirical models. In
addition, radiative transfer models (RTM) were used to produce estimates of
CWC and FMC, which were compared with the empirical ones.
Results revealed that, for all metrics spatial variability was significantly
lower than temporal. Thus we concluded that experimental design should
prioritize sampling frequency rather than sample size. Dm variability was
high which demonstrates that a constant annual Dm value should not be used to
predict EWT from FMC as other previous studies did. Relative root mean
square error (RRMSE) evaluated the performance of nine spectral indices to
compute each variable. Visible Atmospherically Resistant Index (VARI)
provided the lowest explicative power in all cases. For proximal sensing,
Global Environment Monitoring Index (GEMI) showed higher statistical
relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global
Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 %
respectively). When MODIS data were used, results showed an increase in
R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC
(RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT
(RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can
explain these differences as the portion of vegetation observed by MODIS is
larger than when using proximal sensing including the spectral response from
scattered trees and its shadows. CWC was better predicted than the other two
water content metrics, probably because CWC depends on LAI, that shows a
notable seasonal variation in this ecosystem. Strong statistical
relationship was found between empirical models using indices sensible to
chlorophyll activity (NDVI or EVI which are not directly related to water
content) due to the close relationship between LAI, water content and
chlorophyll activity in grassland cover, which is not true for other types
of vegetation such as forest or shrubs. The empirical methods tested
outperformed FMC and CWC products based on radiative transfer model
inversion. |
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