P. Mazzega

Analysis of ERS wind scatterometer time series over Sahel (Mali)

Abstract ERS wind scatterometer (WSC) time series are analyzed over different ecoclimatic regions of the African Sahel during the period 1991–1995. At 45° incidence angle, the strong seasonality of σ o time series can be directly linked to the successive wet and dry seasons. Moreover, the annual σ o dynamic range was found to be strongly correlated to total rainfall. The interpretation of the σ o temporal plots is carried out by combining a backscattering model to a grassland growth model. Results highlight the decreasing contribution of the herbaceous component with latitude. However, its contribution is far from negligible and can reach 60% in the Soudano-Sahelian subzone at peak herbaceous mass. Additionally, the tree layer has a negligible effect on the radar signal at the scale of a resolution cell. Finally, a simple parametric backscattering model is calibrated and used in an inversion process. The resolution of the inverse problem is based on a ‘brute-force’ method that consists of exploring all the combinations of parameters of interest. Despite a poor estimation of the temporal variation of the herbaceous mass B t , the retrieved maximum mass compares well with ground estimates.

Mapping of Sahelian vegetation parameters from ERS scatterometer data with an evolution strategies algorithm

Abstract The West African Sahel rainfall regime is known for its spatio-temporal variability at different scales which has a strong impact on vegetation development. This study presents results of the combined use of a simple water balance model, a radiative transfer model and ERS scatterometer data to produce map of vegetation biomass and thus vegetation cover at a spatial resolution of 25 km. The backscattering coefficient measured by spaceborne wind scatterometers over Sahel shows a marked seasonality linked to the drastic changes of both soil and vegetation dielectric properties associated to the alternating dry and wet seasons. For lack of a direct observation, METEOSAT rainfall estimates are used to calculate temporal series of soil moisture with the help of a water balance model. This a priori information is used as input of the radiative transfer model that simulates the interaction between the radar wave and the surface components (soil and vegetation). Then, an inversion algorithm is applied to retrieve vegetation aerial mass from the ERS scatterometer data. Because of the nonlinear feature of the inverse problem to be solved, the inversion is performed using a global stochastic nonlinear inversion method. A good agreement is obtained between the inverse solutions and independent field measurements with mean and standard deviation of −54 and 130 kg of dry matter by hectare (kg DM/ha), respectively. The algorithm is then applied to a 350,000 km 2 area including the Malian Gourma and Seno region and a Sahelian part of Burkina Faso during two contrasted seasons (1999 and 2000). At the considered resolution, the obtained herbaceous mass maps show a global qualitative consistency ( r 2 =0.71) with NDVI images acquired by the VEGETATION instrument.

Retrieving land surface parameters over Sahel from ERS wind scatterometer data

Wind Scatterometers are active microwave instruments with low spatial resolution and high sampling rate. Recent studies have shown high potentials of these data to monitor land surface parameters over semi-arid areas, including the soil moisture and the vegetation herbaceous mass. The objective of this study is to evaluate the potentialities of the ERS Wind Scatterometer to retrieve land surface parameters. After a brief presentation of the model used for the interpretation of ?° time series, the inverse problem aiming at estimating herbaceous mass and soil moisture time series given the ERS WSC data is analysed. Due to the strong spatial and temporal variability of the soil moisture, the inverse problem appears to be a priori under-determined. We then solve the inverse problem with a “brute force” approach that consists in systematical exploration of the parameter space. This method does not only allow to obtain the optimal solutions like more classical method (generalised least square, simplex), but also the whole domain of admissible solutions. Analysis of this domain provides interesting results for the inverse problem subtle understanding