Neural network models for soil moisture forecasting from remote sensed measurements

Authors

  • Andrea Marini Idea-re S.r.l.
  • Loris Francesco Termite
  • Alberto Garinei
  • Marcello Marconi
  • Lorenzo Biondi

DOI:

https://doi.org/10.21014/acta_imeko.v9i2.797

Abstract

Machine learning techniques are employed to describe the temporal behavior of soil moisture using meteorological data as inputs. Three different Artificial Neural Network models, a feedforward Multi-Layer Perceptron, a Long-Short Term Memory and the Adaptive Network-based Fuzzy Inference System, are trained and their results are compared. The soil moisture is expressed in terms of Soil Water Index, derived from satellite retrievals, with the last known value also being used as input. The results are promising as the proposed methodology relies on free-access data with a worldwide coverage, allowing to easily estimate the forthcoming soil moisture. The knowledge of the expected value of this variable could be extremely useful for irrigation scheduling and it is the basis of Decision Support Systems to efficiently manage water resources in agriculture.

Downloads

Additional Files

Published

2020-06-30

Issue

Vol. 9 No. 2 (2020)

Section

Research Papers

License

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC BY 4.0, Creative Commons Attribution 4.0 International License.

Users are free to

  • share, i.e. copy and redistribute the material in any medium or format for any purpose, even commercially;
  • adapt, i.e. remix, transform, and build upon the material for any purpose, even commercially.

At the same time, the user must give appropriate credit, provide a link to the license, and indicate if changes were made.
Additional information about the license can be found at: https://creativecommons.org/licenses/by/4.0/.

Authors are

  • able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).