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Recent efforts have been concentrated in the development of models to understand and predict the impact of environmental changes on hydrological cycle and water resources in arid and semi-arid regions. In this context, remote sensing data have been widely used to initialize, to force, or to control the simulations of these models. However, for several reasons, including the difficulty in establishing relationships between observational and model variables, the potential offered by satellite data has not been fully used. As a matter of fact, a few hydrological studies that use remote sensing data emanating from different sources (sensors, platforms) have been performed. In this context, the SUDMED programme has been designed in 2002 to address the issue of improving our understanding about the hydrological functioning of the Tensift basin, which is a semi-arid basin situated in central Morocco. The first goal is model development and/or refinement, for investigating the hydrological responses to future scenario about climate change and human pressure. The second aim is the effective use of remote sensing observations in conjunction with process models, to provide operational prognostics for improving water-resource management. The objective of this paper is to present the SUDMED programme, its objectives, and its thrust areas, and to provide an overview of the results obtained in the first phase of the programme (2002-2006). Finally, the lessons learned, future objectives, and unsolved issues are presented. [ABSTRACT FROM AUTHOR]

Dynamics of snow in semi-arid mountains are poorly investigated despite the fact that snow may represent an important source of water for downstream populations especially in the spring and early summer. Data acquired by space-borne optical sensors (i.e. reflectance and derived snow indices) may be suitable for spatial and temporal monitoring of snow cover. However, due to prevailing terrain and climatic conditions, the use of satellite sensor data to monitor snow dynamics is not trivial over such regions. Snowfall as well as precipitation are characterized by strong space-time variability. Indeed snow can fall and melt within one week. Under such conditions, appropriate monitoring of snow dynamics requires space instruments that provide data with high spatial and high temporal resolutions. In this context we developed a new approach based on the combination of two types of instruments: low spatial and high temporal resolution (Systéme Pour l'Observation de la Terre (SPOT)-VEGETATION) and high spatial and low temporal resolution (Landsat Thematic Mapper). This new approach improves the relationship between snow index and snow area. The method is validated against snow maps derived from classification of high spatial resolution data on the Atlas range, in Morocco. It is then applied to a one-year series of SPOT-4 VEGETATION images allowing to derive a temporal snow cover profile at 1 km spatial resolution over the entire Atlas, The yearly snow profile obtained is of great interest for hydrological modelling. [ABSTRACT FROM AUTHOR]

The VEGETATION instrument is the starting point of a European Earth monitoring system that was developed jointly by France, the European Commission, Belgium, Italy and Sweden. Since April 1998, VEGETATION has provided a high quality global monitoring of the day-to-day land cover dynamics at 1 km resolution. The whole dataset is now available free of charge to the broad range of potential users and applications. The quality of delivered products in terms of radiometry, geometry and additional processing for directional and atmospheric effects stands VEGETATION as an excellent tool for the monitoring of surface hydrology, crops, forest and land cover. Surface reflectances that are delivered by the operational VEGETATION system are corrected for molecular and aerosol scattering, for water vapour, ozone and other gas absorption. So far, the only well known Maximum Value Composite (MVC) technique was used in the construction of 10-day synthesis. An additional enhanced composite product is now available, which evaluates the atmospheric optical depth and normalizes angular Sun-target-sensor variations. After presenting the VEGETATION instrument and products, this paper introduces the new compositing schemes ( Duchemin and Maisongrande 2002, Duchemin et al. 2002 ) and presents samples of the new products. [ABSTRACT FROM AUTHOR]