This article aims to analyze agronomic drought in a highly anthropogenic semi-arid region. This is the western Mediterranean region. The study uses satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Scatterometer (ASCAT) describing the dynamics of vegetation cover and soil water content through the Normalized Difference Vegetation Index (NDVI) and the Soil Water Index (SWI). An analysis of the vegetation anomaly index (VAI) highlights the difference between agricultural and natural areas. Thus, two land use classes are considered for the analysis of drought indices, agricultural areas and natural areas. The contribution of vegetation cover (VAI) was combined with the effect of soil water content using the moisture anomaly index (MAI) through a new drought index called the global drought index (GDI). This index considers the seasonal effect of the development of vegetation cover and soil water content with variable weightings over time for the two indices VAI and MAI.
This study presents a strategy to improve the evapotranspiration estimates in semi arid areas using data assimilation in a
SVAT (Soil Vegetation Atmosphere Transfer) modeling, the ISBA scheme (Interaction Soil Biosphere Atmosphere). In
the perspective to use remote sensing products, the overall objective of this work is to identify the best combination of
data (surface soil moisture / surface temperature / evapotranspiration), the temporal repetitiveness of acquisition (daily /
tri-daily / weekly / bi-monthly / monthly) and the kind of data assimilation technique (two dimensional variational
method / Extended Kalman filter) to constraint evapotranspiration predictions. Within this preliminary study, synthetic
data referring to a wheat crops experimental site located in the Haouz Plain, part of the Tensift basin near Marrakesh in
Morocco have been used (from January to May 2003). The results show that in order to improve the evapotranspiration
through the analysis of the root zone soil moisture, the surface soil moisture is the most informative observation to use in
the assimilation process (roughly 40% improvement in evapotranspiration RMSE). Combinations of observations
improve the results but not significantly (few % improvement in evapotranspiration RMSE). Assimilation is very
efficient for short assimilation windows. It is also shown that the propagation of the background error matrix done
through the Extended Kalman filter doesn’t represent a significant added value with regards to the constant matrix used
with two dimensional variational method.
V. Simonneaux, A. Abourida, A. Boudhar, A. Cheggour, A. Chaponnière, B. Berjamy, G. Boulet, A. Chehbouni, L. Drapeau, B. Duchemin, S. Erraki, J. Ezzahar, R. Escadafal, N. Guemouria, L. Hanich, L. Jarlan, H. Kharrou, S. Khabba, M. Le Page, S. Mangiarotti, O. Merlin, B. Mougenot, A. Mokssit, A. Ouldbba
The SudMed project aims since 2002 at modelling the hydrological cycle in the Tensift semi arid watershed located in
central Morocco. To reach these modelling objectives, emphasis is put on the use of high and low resolution remote
sensing data, in the visible, near infrared, thermal, and microwave domains, to initialize, to force or to control the
implementation of the process models. Fundamental studies have been conducted on Soil-Vegetation-Atmosphere
Transfer modelling (SVAT), especially related to the various means of incorporating both ground and remote sensing
observation into them. Satellite data have been used for monitoring the snow dynamic which is a major contribution to
runoff issued from the mountains. Remote sensing image time series have also been used to map the land cover, based
on NDVI time profiles analysis or temporal unmixing of low resolution pixels. Subsequently, remote sensing time series
proved to be very valuable for monitoring the development of vegetation and the crop water status, in order to estimate of evapotranspiration, key information for irrigation management.
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