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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]

The rationale of this research is to investigate approaches based on modelling and remote sensing data for estimating the spatial distribution of yield and irrigation of wheat in semi-arid areas. The specific objective is to compare the performances of two approaches to test the STICS crop model using remotely sensed estimates of leaf area index (LAI). An experimental study of phenology, yield and water balance of irrigated wheat was made in the Marrakech-Haouz plain during year 2003. Experimental data was allowed to run STICS using two approaches: (1) Calibration of the parameters that control the time course of LAI; (2) driving from LAI time series interpolated with a simple model. The results show the accuracy of STICS to simulate actual evapotranspiration and yield for both approaches. Finally, the two approaches were compared using remotely sensed estimates of LAI upon four scenarios of satellite time revisit frequency. The simulations we obtained always show acceptable results. However, differences appear between the variables, between the approaches and between the frequencies. [ABSTRACT FROM AUTHOR]

Abstract: The monitoring of crop production and irrigation at a regional scale can be based on the use of ecosystem process models and remote sensing data. The former simulate the time courses of the main biophysical variables which affect crop photosynthesis and water consumption at a fine time step (hourly or daily); the latter allows to provide the spatial distribution of these variables over a region of interest at a time span from 10 days to a month. In this context, this study investigates the feasibility of using the normalised difference vegetation index (NDVI) derived from remote sensing data to provide indirect estimates of: (1) the leaf area index (LAI), which is a key-variable of many crop process models; and (2) crop coefficients, which represent the ratio of actual (AET) to reference (ET0) evapotranspiration. A first analysis is performed based on a dataset collected at field in an irrigated area of the Haouz plain (region of Marrakesh, Central Morocco) during the 2002–2003 agricultural season. The seasonal courses of NDVI, LAI, AET and ET0 have been compared, then crop coefficients have been calculated using a method that allows roughly to separate soil evaporation from plant transpiration. This allows to compute the crop basal coefficient (K cb) restricted to the plant transpiration process. Finally, three relationships have been established. The relationships between LAI and NDVI as well as between LAI and K cb were found both exponential, with associated errors of 30% and 15%, respectively. Because the NDVI saturates at high LAI values (>4), the use of remotely-sensed data results in poor accuracy of LAI estimates for well-developed canopies. However, this inaccuracy was not found critical for transpiration estimates since AET appears limited to ET0 for well-developed canopies. As a consequence, the relationship between NDVI and K cb was found linear and of good accuracy (15%). Based on these relationships, maps of LAI and transpiration requirements have been derived from two Landsat7-ETM+ images acquired at the beginning and the middle of the agricultural season. These maps show the space and time variability in crop development and water requirements over a 3km×3km irrigated area that surrounds the fields of study. They may give an indication on how the water should be distributed over the area of interest in order to improve the efficiency of irrigation. The availability, in the near future, of Earth Observation Systems designed to provide both high spatial resolution (10m) and frequent revisit (day) would make it feasible to set up such approaches for the operational monitoring of crop phenology and irrigation at a regional scale. [Copyright &y& Elsevier]

Abstract: This study was performed to test three methods based on the FAO-56 “dual” crop coefficient approach to estimate actual evapotranspiration (AET) for winter wheat under different irrigation treatments in the semi-arid region of Tensift Al Haouz, Marrakech (center of Morocco). The three methods differ in the calculation of the basal crop coefficient (K cb) and the fraction of soil surface covered by vegetation (f c). The first approach strictly follows the FAO-56 procedure, with K cb given in the FAO-56 tables and f c calculated from K cb (No-Calibration method). The second method uses local K cb and f c values estimated from field measurements (Local-Calibration method) and the last approach uses a remotely-sensed vegetation index to estimate K cb and f c (NDVI-Calibration method). The analysis was performed on three fields using actual (AET) measured by Eddy Correlation systems. It was shown that the Local-Calibration approach gave best results. Accurate estimates of K cb and f c were necessary for FAO-56 “dual” crop coefficient application. The locally derived K cb for winter wheat taken at initial, mid-season, and maturity crop growth were 0.15, 0.90 and 0.23, respectively. The K cb value at the mid-season stage was found to be considerably less than that suggested by the FAO-56. Similarity between the seasonal pattern of normalized difference vegetation index (NDVI) and K cb showed potential for modelling NDVI into a K cb. The obtained relationships between K cb and NDVI, and between f c and NDVI could be easily incorporated within the FAO-56 “dual” crop coefficient model and, thereby, provide a means to apply remotely sensed observation for real-time wheat irrigation scheduling. The results obtained were very acceptable especially when the soil evaporation is negligible. Therefore, the K cb–NDVI relationship employed in the FAO-56 “dual” crop coefficient model holds great potential for estimating crop water requirements on an operational basis and consumption at a regional scale. [Copyright &y& Elsevier]