@article{fdi:010042571,
  title = {{E}volutionary bi-objective optimization of a semi-arid vegetation dynamics model with {NDVI} and sigma(0) satellite data},
  author = {{M}angiarotti, {S}. and {M}azzega, {P}. and {J}arlan, {L}. and {M}ougin, {E}ric and {B}aup, {F}. and {D}emarty, {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}atellite radar backscattering coefficient sigma(0) data from {ENVISAT}-{ASAR} and {N}ormalized {D}ifference {V}egetation {I}ndex ({NDVI}) data from {SPOT}-{VEGETATION} are assimilated in the {STEP} model of vegetation dynamics. {T}he {STEP} model is coupled with a radiative transfer model of the radar backscattering and {NDVI} signatures of the soil and herbaceous vegetation. {T}hese models are driven by field data (rainfall time series, soil properties, etc.). {W}hile some model parameters have fixed values, some other parameters have target values to be optimized. {T}he study focuses on a well documented 1 km(2) homogeneous area in a semi-arid region ({G}ourma, {M}ali). {W}e 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. {F}or this purpose we introduce an evolutionary strategy ({ES}) approach relying on a bi-objective function to be minimized in the data assimilation/inversion process. {S}everal 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. {I}t is shown that the bi-objective {ES} leads to improved model predictions and also to a better readability of the results by exploring the {P}areto front of optimal and admissible solutions. {I}t 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. {H}owever some systematic biases on the biomass predictions (errors in the range 140 to 300 kg ha(-1)) are observed. {T}hanks 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.},
  keywords = {assimilation ; multi objective optimization ; {P}areto ; vegetation dynamics model ; evolutionary strategy ; {NDVI} ; radar backscattering coefficient},
  booktitle = {},
  journal = {{R}emote {S}ensing of {E}nvironment},
  volume = {112},
  numero = {4},
  pages = {1365--1380},
  ISSN = {0034-4257},
  year = {2008},
  DOI = {10.1016/j.rse.2007.03.030},
  URL = {https://www.documentation.ird.fr/hor/fdi:010042571},
}