Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations.

ElSaadani M, Habib E, Abdelhameed AM, Bayoumi M.

Open source

DOI: 10.3389/frai.2021.636234
Published: 2021-03-04
Container: Front Artif Intell
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/frai.2021.636234,
  title = {Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations.},
  author = {ElSaadani M and  Habib E and  Abdelhameed AM and  Bayoumi M.},
  year = {2021},
  journal = {Front Artif Intell},
  doi = {10.3389/frai.2021.636234},
  url = {https://doi.org/10.3389/frai.2021.636234}
}

RIS

TY  - JOUR
TI  - Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations.
AU  - ElSaadani M
AU  -  Habib E
AU  -  Abdelhameed AM
AU  -  Bayoumi M.
PY  - 2021
JO  - Front Artif Intell
DO  - 10.3389/frai.2021.636234
UR  - https://doi.org/10.3389/frai.2021.636234
ER  -

APA

M, E., E, H., AM, A., & M., B. (2021). Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations.. Front Artif Intell. https://doi.org/10.3389/frai.2021.636234

Source records

europe-pmc · retrieved 2026-09-25T13:15:01.220Z
doaj · retrieved 2026-09-25T13:15:01.234Z