Abstract :
[en] By governing water transfer between vegetation and atmosphere, evapotranspiration
(ET) can have a strong influence on crop yields. An estimation of ET from remote
sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land
Surface Analysis (LSA). This ET product is obtained operationally every 30 min using
a simplified SVAT scheme that uses, as input, a combination of remotely sensed data
and atmospheric model outputs. The standard operational mode uses other LSA-SAF
products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave
flux, and the downwelling surface longwave flux), meteorological data, and the
ECOCLIMAP database to identify and characterize the land cover.
With the overall objective of adapting this ET product to crop growth monitoring
necessities, this study focused first on improving the ET product by integrating
crop-specific information from high and medium spatial resolution remote-sensing data.
A Landsat (30 m)-based crop type classification is used to identify areas where the target
crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging
Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be
extracted. The SVAT model was run for 1 year (2007) over a study area covering
Belgium and part of France using this supplementary information. Results were compared
to those obtained using the standard operational mode.
ET results were also compared with ground truth data measured in an eddy covariance
station. Furthermore, transpiration and potential transpiration maps were retrieved
and compared with those produced using the Crop Growth Monitoring System (CGMS),
which is run operationally by the European Commission’s Joint Research Centre to produce
in-season forecast of major European crops. The potential of using ET obtained
from remote sensing to improve crop growth modelling in such a framework is studied
and discussed.
Finally, the use of the ET product is also explored by integrating it in a simpler modelling
approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach
(CASA) agroecosystem model was therefore applied to obtain net primary production,
dry matter productivity, and crop yield using only LSA-SAF products. The values of
yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO).
Results showed the potential of using this simplified remote-sensing method for crop
monitoring.
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