The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "M. H. Afshar and M. T. Yilmaz",
  title =        "The added utility of nonlinear methods compared to
                 linear methods in rescaling soil moisture products",
  journal =      "Remote Sensing of Environment",
  volume =       "196",
  pages =        "224--237",
  year =         "2017",
  ISSN =         "0034-4257",
  DOI =          "doi:10.1016/j.rse.2017.05.017",
  URL =          "",
  abstract =     "In this study, the added utility of nonlinear
                 rescaling methods relative to linear methods in the
                 framework of creating a homogenous soil moisture time
                 series has been explored. The performances of 31 linear
                 and nonlinear rescaling methods are evaluated by
                 rescaling the Land Parameter Retrieval Model (LPRM)
                 soil moisture datasets to station-based watershed
                 average datasets obtained over four United States
                 Department of Agriculture (USDA) Agricultural Research
                 Service (ARS) watersheds. The linear methods include
                 first-order linear regression, multiple linear
                 regression, and multivariate adaptive regression
                 splines (MARS), whereas the nonlinear methods include
                 cumulative distribution function matching (CDF),
                 artificial neural networks (ANN), support vector
                 machines (SVM), Genetic Programming (GEN), and copula
                 methods. MARS, GEN, SVM, ANN, and the copula methods
                 are also implemented to use lagged observations to
                 rescale the datasets. The results of a total of 31
                 different methods show that the nonlinear methods
                 improve the correlation and error statistics of the
                 rescaled product compared to the linear methods. In
                 general, the method that yielded the best results using
                 training data improved the validation correlations, on
                 average, by 0.063, whereas ELMAN ANN and GEN, using
                 lagged observations methods, yielded correlation
                 improvements of 0.052 and 0.048, respectively. The
                 lagged observations improved the correlations when they
                 were incorporated into rescaling equations in linear
                 and nonlinear fashions, with the nonlinear methods
                 (particularly SVM and GEN but not ANN and copula)
                 benefitting from these lagged observations more than
                 the linear methods. The overall results show that a
                 large majority of the similarities between the LPRM and
                 watershed average datasets are due to linear relations;
                 however, nonlinear relations clearly exist, and the use
                 of nonlinear rescaling methods clearly improves the
                 accuracy of the rescaled product.",
  keywords =     "genetic algorithms, genetic programming, Soil
                 moisture, Rescaling, Linear, Nonlinear, Remote

Genetic Programming entries for Mehdi Hesami Afshar Mustafa Tolga Yilmaz