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| Titel |
Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes |
| VerfasserIn |
E. Boegh, R. Houborg, J. Bienkowski, C. F. Braban, T. Dalgaard, N. Dijk, U. Dragosits, E. Holmes, V. Magliulo, K. Schelde, P. Tommasi, L. Vitale, M. R. Theobald, P. Cellier, M. A. Sutton |
| 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 ; 10, no. 10 ; Nr. 10, no. 10 (2013-10-07), S.6279-6307 |
| Datensatznummer |
250085350
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| Publikation (Nr.) |
 copernicus.org/bg-10-6279-2013.pdf |
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| Zusammenfassung |
| Leaf nitrogen and leaf surface area influence the exchange of gases between
terrestrial ecosystems and the atmosphere, and play a significant role in the
global cycles of carbon, nitrogen and water. The purpose of this study is to
use field-based and satellite remote-sensing-based methods to assess leaf
nitrogen pools in five diverse European agricultural landscapes located in
Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy.
REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse
canopy radiative transfer modelling system which has shown proficiency for
regional mapping of leaf area index (LAI) and leaf chlorophyll
(CHLl) using remote sensing data. In this study, high spatial
resolution (10–20 m) remote sensing images acquired from the multispectral
sensors aboard the SPOT (Satellite For Observation of Earth) satellites were
used to assess the capability of REGFLEC for mapping spatial variations in
LAI, CHLland the relation to leaf nitrogen (Nl) data
in five diverse European agricultural landscapes. REGFLEC is based on
physical laws and includes an automatic model parameterization scheme which
makes the tool independent of field data for model calibration. In this
study, REGFLEC performance was evaluated using LAI measurements and
non-destructive measurements (using a SPAD meter) of leaf-scale
CHLl and Nl concentrations in 93 fields representing
crop- and grasslands of the five landscapes. Furthermore, empirical
relationships between field measurements (LAI, CHLl and
Nl and five spectral vegetation indices (the Normalized
Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation
Index-2, the Green Normalized Difference Vegetation Index, and the green
chlorophyll index) were used to assess field data coherence and to serve as a
comparison basis for assessing REGFLEC model performance. The field
measurements showed strong vertical CHLl gradient profiles in
26% of fields which affected REGFLEC performance as well as the
relationships between spectral vegetation indices (SVIs) and field
measurements. When the range of surface types increased, the REGFLEC results
were in better agreement with field data than the empirical SVI regression
models. Selecting only homogeneous canopies with uniform CHLl
distributions as reference data for evaluation, REGFLEC was able to explain
69% of LAI observations (rmse = 0.76), 46% of measured canopy
chlorophyll contents (rmse = 719 mg m−2) and 51% of measured
canopy nitrogen contents (rmse = 2.7 g m−2). Better results were
obtained for individual landscapes, except for Italy, where REGFLEC performed
poorly due to a lack of dense vegetation canopies at the time of satellite
recording. Presence of vegetation is needed to parameterize the REGFLEC
model. Combining REGFLEC- and SVI-based model results to minimize errors for
a "snap-shot" assessment of total leaf nitrogen pools in the five
landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf
nitrogen pools between landscapes are attributed to seasonal variations,
extents of agricultural area, species variations, and spatial variations in
nutrient availability. In order to facilitate a substantial assessment of
variations in Nl pools and their relation to landscape based
nitrogen and carbon cycling processes, time series of satellite data are
needed. The upcoming Sentinel-2 satellite mission will provide new multiple
narrowband data opportunities at high spatio-temporal resolution which are
expected to further improve remote sensing capabilities for mapping LAI,
CHLl and Nl. |
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