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On the spatio-temporal dynamics of soil moisture at the field scale
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“TLMs are deployed in smart agriculture systems to interpret sensor data on soil moisture [146], temperature, and humidity, allowing automated irrigation systems to respond to natural language commands.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [146]
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Metadata
- Authors
- Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., & Vanderborght, J.
- Affiliations
- Forschungszentrum Jülich (DE)
- Agricultural Research Service (US)
- Corresponding author
- Vereecken, H.
- ORCID
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 516
- Pages
- 76-96
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2070817134
- Cited by
- 584
- References
- 302
- Citations by year
- 2026: 43 · 2025: 57 · 2024: 51 · 2023: 58 · 2022: 71 · 2021: 58 · 2020: 51 · 2019: 44 · 2018: 40 · 2017: 40 · 2016: 32 · 2015: 28 · 2014: 11
- FWCI
- 19.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Hydrology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Physical Sciences › Engineering › Civil and Structural Engineering › Soil and Unsaturated Flow
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Geostatistics and Mapping
- Keywords
- soil moisture, spatiotemporal dynamics, field scale, soil moisture variability, remote sensing of soil moisture, temporal stability analysis, wavelet analysis, empirical orthogonal functions
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