Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval

Soo See Chai, Bert Veenendaal, Geoff West, Jeffrey P. Walker

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)

Abstract

The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "similarity" on the accuracy of the network outputs. In this paper, an ANN model is used to retrieve soil moisture from the H- and V-polarized brightness temperature obtained. The research discussed in this paper is focused on the standard deviation of the data used for training and testing of the ANN. It is shown that similarity in standard deviation is a good indicator to choose representative training and testing data set. By doing this, the accuracy of retrieval increases from around 22% volume/volume (v/v) of Root Mean Square Error (RMSE) to around 2%(v/v).

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Edition1
DOIs
Publication statusPublished - 1 Dec 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States of America
Duration: 6 Jul 200811 Jul 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume2

Conference

Conference2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
CountryUnited States of America
CityBoston, MA
Period6/07/0811/07/08

Keywords

  • Artificial neural networks
  • Backpropagation
  • Passive microwave
  • Soil moisture content

Cite this

Chai, S. S., Veenendaal, B., West, G., & Walker, J. P. (2008). Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval. In 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings (1 ed.). [4779087] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2, No. 1). https://doi.org/10.1109/IGARSS.2008.4779087