Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks

J. F.Lavado Contador, M. Maneta, S. Schnabel

Research output: Contribution to journalArticlepeer-review

Abstract

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.

Original languageEnglish
Pages (from-to)211-230
Number of pages20
JournalEnvironmental Monitoring and Assessment
Volume121
Issue number1-3
DOIs
StatePublished - Oct 2006

Keywords

  • Dehesa
  • Forecasting soil moisture
  • Sampling
  • Topographic variables

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