Recurrent Neural Networks for Soil Moisture Prediction Leveraging Soil Matric Potential Data

Authors

Mattia Barezzi (ORCID: https://orcid.org/0000-0002-6596-0497)
Renato Ferrero (ORCID: https://orcid.org/0000-0003-4459-4843)
Antonio Costantino Marceddu (ORCID: https://orcid.org/0000-0003-4843-1267)
Umberto Garlando (ORCID: https://orcid.org/0000-0002-3364-7017)
Nicola Dilillo (ORCID: https://orcid.org/0009-0002-6395-9342)

Institutions

Politecnico di Torino (IT)

Publication Details

Published: 2024-09-26
DOI: https://doi.org/10.1109/cafe63183.2024.11069355
Citations: 1
Primary Topic: Soil Moisture and Remote Sensing
Type: article
Field-Weighted Citation Impact: 0.21

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article

Recurrent Neural Networks for Soil Moisture Prediction Leveraging Soil Matric Potential Data

Mattia Barezzi, Renato Ferrero, Antonio Costantino Marceddu, Umberto Garlando et al.

Sep 26, 2024

1 citations

Soil Moisture and Remote Sensing

0.21

article

Recurrent Neural Networks for Soil Moisture Prediction Leveraging Soil Matric Potential Data

Mattia Barezzi, Renato Ferrero, Antonio Costantino Marceddu, Umberto Garlando, Nicola Dilillo

September 26, 2024article en

1 citations

Abstract

No abstract available for this paper.

Politecnico di Torino (IT)

Zero hunger

Openalex Percentile: Top 47%

Soil Moisture and Remote Sensing

0.21

DOI Source OpenAlex

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Recurrent Neural Networks for Soil Moisture Prediction Leveraging Soil Matric Potential Data — Mattia Barezzi, Renato Ferrero, et al. · (2024) | TGRS Research Map | TGRS