Spatio-temporal soil moisture estimation using neural network with wavelet preprocessing

Ajla Kulaglic, B. Berk Ustundag

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Soil moisture is an important indicator that defines the land surface-atmosphere interactions by contributing precisely to the surface energy and water balance. In this study, we examine the utility of the Neural Network (NN) model using Discrete Wavelet Transform (DWT) as preprocessing mechanism for soil moisture estimation. The decomposed wavelet sub-time series data was used as input to the 3-layered NN. Recognition of, as well as understanding the changes and spatial distributions of soil moisture are crucial in order to determine water usage, droughts, floods and surface runoffs. This study aims to use remote sensing data together with ground-based agro-meteorological data. The soil moisture data from sites were composed of the 15- A nd 45-cm, measured at intervals of 8 days during the period of crop growth season (October to June) between 2014 and 2015. Simultaneously, soil moisture data were selected as remotely sensed images were acquired. Utilizing remotely sensed data (Landsat 7 and Landsat 8) Vegetation Indices (VI): Landsat Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Modified Soil Adjusted Vegetation Index (MSAVI) were obtained. Temperature Vegetation Dryness Index (TVDI) was computed with respect to LST. The results of this study showed that the proposed model, using ground-based and remotely sensed data, should serve as an enhanced method to obtain highly reliable soil moisture values.

Original languageEnglish
Title of host publication2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538638842
DOIs
Publication statusPublished - 19 Sept 2017
Event6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017 - Fairfax, United States
Duration: 7 Aug 201710 Aug 2017

Publication series

Name2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017

Conference

Conference6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017
Country/TerritoryUnited States
CityFairfax
Period7/08/1710/08/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • discrete wavelet transformation
  • EVI
  • LST
  • MSAVI
  • NDVI
  • neural network
  • soil moisture
  • TVDI
  • vegetation indices

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