Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space

@inproceedings{Ines2008ParameterCW,
  title={Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space},
  author={Amor V. M. Ines and Binayak P. Mohanty},
  year={2008}
}
[1] The estimation of effective soil hydraulic parameters and their uncertainties is a critical step in all large-scale hydrologic and climatic model applications. Here a scale-dependent (top-down) parameter estimation (inverse modeling) scheme called the noisy Monte Carlo genetic algorithm (NMCGA) was developed and tested for estimating these effective soil hydraulic parameters and their uncertainties. We tested our method using three case studies involving a synthetic pixel (pure and mixed… CONTINUE READING
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