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Titel |
Upscaling instantaneous to daily evapotranspiration using modelled daily
shortwave radiation for remote sensing applications: an artificial neural
network approach |
VerfasserIn |
Loise Wandera, Kaniska Mallick, Gerard Kiely, Olivier Roupsard, Mathias Peichl, Vincenzo Magliulo |
Konferenz |
EGU General Assembly 2017
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Medientyp |
Artikel
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Sprache |
en
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 19 (2017) |
Datensatznummer |
250150041
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Publikation (Nr.) |
EGU/EGU2017-14464.pdf |
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Zusammenfassung |
Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to
daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting
sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be
the most robust reference variable explaining the ratio between ETd and ETi . This
study aims to contribute in ETi upscaling for global studies using the ratio between
daily and instantaneous incoming shortwave radiation (RSd / RSi) as a factor for
converting ETi to ETd. This paper proposes an artificial neural network (ANN)
machine-learning algorithm first to predict RSd from RSi followed by using the RSd
/ RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using
RSi and RSd observations from multiple sub-networks of the FLUXNET database
spread across different climates and biomes (to represent inputs that would typically
be obtainable from remote sensors during the overpass time) in conjunction with
some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric
shortwave radiation), we developed the ANN model for reproducing RSd and further
used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different
morning and afternoon times of day, under varying sky conditions, and also at different
geographic locations. RS-based upscaled ETd produced a significant linear relation (R 2
= 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m−2 d −1 ; approx. 4 %), and good
agreement (RMSE 1.55 to 1.86 MJ m−2 d −1 ; approx. 10 %) with the observed
ETd, although a systematic overestimation of ETd was also noted under persistent
cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN
training reduced the systematic overestimation tendency in predominantly overcast
days. An intercomparison with existing upscaling method at daily, 8-day, monthly,
and yearly temporal resolution revealed a robust performance of the ANNdriven
RS-based ETi upscaling method and was found to produce lowest RMSE under
cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to
biome selection and high ETd prediction errors in forest ecosystems are primarily
associated with greater rainfall and cloudiness. The overall methodology appears to
be promising and has substantial potential for upscaling ETi to ETd for field and
regional-scale evapotranspiration mapping studies using polar orbiting satellites. |
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