Estimation of regional evapotranspiration and biomass production from remote sensing data by artificial neural network (ANN) method

Xiangqun Zheng, Fengju Shen, Shunan Zheng, Eli Argaman, Dan Blumberg, Jiftah Ben-Asher, Shira Amir

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This work was conducted for predicting regional evapotranspiration and biomass density by artificial neural network (ANN) method under varying climate conditions. A feed forward neural network with a set of landsat data including temperature and short wave radiation data was trained from the North part of the Jordan River Basin in Israel to predict actual evapotranspiration. The fitted results manifested that comparative analysis of the backpropagation (BP) and radial basis function (RBF) networks provided very similar data. BP would spend much more training time than RBF, which could get convergences rapidly. Nevertheless, the RBF still had some problems in the prediction, as it was not prompt in cases dealing with highdimensional input spaces, especially when the original data set contained some invalid variables. Therefore, principle component analysis was used to correct the input data. Above all, the BP and RBF can be used as perfect tools for taking the place of other mathematical models to predict the evapotranspiration (ET) and weighted difference vegetation index (WDVI). This work approved that ANN method can effectively predict regional evapotranspiration and distribution of vegetation under realistic conditions.

Original languageEnglish
Pages (from-to)1558-1561
Number of pages4
JournalJournal of Food, Agriculture and Environment
Volume10
Issue number3-4
StatePublished - 1 Jan 2012

Keywords

  • Artificial neural network
  • Back-propagation
  • Evapotranspiration
  • Landsat
  • Radial basis function

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