EGU21-2306
https://doi.org/10.5194/egusphere-egu21-2306
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Contribution of remote sensing and auxiliary variables in the study of the evolution of periods of droughts

Nesrine Farhani1,2, Gilles Boulet2, Julie Carreau3, Zeineb Kassouk1, Michel Le Page2, Zohra Lili Chabaane1, and Rim Zitouna4
Nesrine Farhani et al.
  • 1Université de Carthage, Institut National Agronomique de Tunisie, Lr GREEN-TEAM, 43 avenue Charles Nicolle, Tunis, Tunisie (farhani.nesrine@gmail.com)
  • 2Centre d’Etudes Spatiales de la Biosphère, Université de Toulouse, CNRS, CNES, IRD, UPS, INRAE, Toulouse, France
  • 3HydroSciences Montpellier (HSM), CNRS/IRD/UM1/UM2, Place Eugène Bataillon, 34095 Montpellier, France
  • 4INRGREF-LRVENC, Carthage University, BP 10 El Menzah IV, 1004 Tunis, Tunisia

In semi-arid areas, plant water use and plant water stress can be derived over large
areas from remotely sensed evapotranspiration estimates. Those can help us to monitor the
impact of drought on the agro- and ecosystems. Both variables can be simulated by a dual
source energy balance model that relies on meteorological variables (air temperature, relative
humidity, wind speed and global radiation) and remote sensing data (surface temperature,
NDVI, albedo and LAI). Surface temperature acquired in the Thermal InfraRed (TIR) domain
is particularly informative for monitoring agrosystem health and adjusting irrigation
requirements. However, available meteorological observations period may often be
insufficient to account for the variability present in the study area. Statistical downscaling
methods applied to reanalysis data can serve to generate surrogate series of meteorological
variables that either fill the gaps in the observation period or extend the observation period in
the past. For this aim, a stochastic weather generator (SWG) is adapted in order to compute
temporal extension of multiple meteorological variables. This surrogate series is then used to
constrain the dual-source model Soil Plant Atmosphere and Remote Evapotranspiration
(SPARSE). Stress index anomalies retrieved from SPARSE are then compared to anomalies in
other wave lengths in order to assess their capacity to detect incipient water stress and early
droughts at the kilometer resolution. Those are the root zone soil moisture at low resolution
derived from the microwave domain, and active vegetation fraction cover deduced from
NDVI time series.

How to cite: Farhani, N., Boulet, G., Carreau, J., Kassouk, Z., Le Page, M., Lili Chabaane, Z., and Zitouna, R.: Contribution of remote sensing and auxiliary variables in the study of the evolution of periods of droughts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2306, https://doi.org/10.5194/egusphere-egu21-2306, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.