Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and sigma(0) satellite data
Résumé
Satellite radar backscattering coefficient sigma(0) data from ENVISAT-ASAR and Normalized Difference Vegetation Index (NDVI) data from SPOT-VEGETATION are assimilated in the STEP model of vegetation dynamics. The STEP model is coupled with a radiative transfer model of the radar backscattering and NDVI signatures of the soil and herbaceous vegetation. These models are driven by field data (rainfall time series, soil properties, etc.). While some model parameters have fixed values, some other parameters have target values to be optimized. The study focuses on a well documented 1 km(2) homogeneous area in a semi-arid region (Gourma, Mali). We here investigate whether departures between model predictions and the corresponding data result from field data errors, in situ data lack of representativeness or some model shortcomings. For this purpose we introduce an evolutionary strategy (ES) approach relying on a bi-objective function to be minimized in the data assimilation/inversion process. Several numerical experiments are conducted, in various mono-objective and bi-objective modes, and the performances of the model predictions compared in terms of NDVI, backscattering coefficient, leaf area index (LAI) and biomass. It is shown that the bi-objective ES leads to improved model predictions and also to a better readability of the results by exploring the Pareto front of optimal and admissible solutions. It is also shown that the information brought from the optical sensor and the radar is coherent; that the corresponding radiative transfer models are also coherent; that the representativeness of in situ data can be compared to satellite data through the modeling process. However some systematic biases on the biomass predictions (errors in the range 140 to 300 kg ha(-1)) are observed. Thanks to the bi-objective ES, we are able to identify some likely shortcoming in the vegetation dynamics model relating the LAI to the biomass variables.