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The AMMA-CATCH Gourma observatory site in Mali: 7The experimental strategy includes deployment of a variety of instruments, from local to 8 meso-scale, dedicated to monitoring and documentation of the major variables characterizing 9 the climate forcing, and the spatio-temporal variability of surface processes and state 10 variables such as vegetation mass, leaf area index (LAI), soil moisture and surface fluxes. 11This paper describes the Gourma site, its associated instrumental network and the research 12 activities that have been carried out since 1984. In the AMMA project, emphasis is put on the 13 relations between climate, vegetation and surface fluxes. However, the Gourma site is also 14 important for development and validation of satellite products, mainly due to the existence of 15 large and relatively homogeneous surfaces. The social dimension of the water resource uses 16 and governance is also briefly analyzed, relying on field enquiry and interviews. 18The climate of the Gourma region is semi-arid, daytime air temperatures are always high and 29Land surface in the Gourma is characterized by rapid response to climate variability, strong

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.

Remote sensing is an important aid in mapping and surveying salt affected soils. Both spatial and temporal variation can be followed. We emphasize this overview on reflectance properties by sunlight. Thermal infrared information, which can be used to detect hygroscopic characteristics of salts, and microwaves, which give indirectly information on salt are only described briefly.Spectral properties of different salts consisting of chlorides and sulphates are presented. Also calcite spectra are evaluated. This evaluation gives the background to examine possibilities of present day operational satellites and new technologies like imaging spectroscopy. Possibilities of operational systems, working in broad bands are treated separately with direct observations on bare soils and indirect observations on vegetation covered surfaces. In the visible part of the spectrum the high reflectance of salt covered areas is prominent. Bands in the middle infrared give information on moisture content, which is often associated with salt content differences, and some information on type of salts. The overview ends with a discussion on possibilities of future remote sensing system.

Abstract.Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil-vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m −2 from values of the order of 50-80 W m −2 ), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution.

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Venμs satellite after its launch expected in early 2010. Every two days, the Venμs mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 nm to 910 nm. The sun-synchronous Venμs orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time.The algorithm that will be implemented for the atmospheric correction of Venμs data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Venμs will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions: -Aerosol optical properties vary quickly with time but slowly with location.-Surface reflectances vary quickly with location but slowly with time.Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances. This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction.

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 program 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 program, its objectives and its thrust areas, and to provide an overview of the results obtained in the first phase of the program (2002)(2003)(2004)(2005)(2006). Finally, the lessons learned, future objectives and the unsolved issues are presented.

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