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Research on Soil Moisture and Irrigation, 2nd Edition
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Research on Soil Moisture and Irrigation, 2nd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Soil and Water".

Deadline for manuscript submissions: 29 September 2026 | Viewed by 967

Special Issue Editors

Dr. Zhe Gu

E-Mail Website
Guest Editor
College of Agricultural Science and Engineering, Hohai University, Nanjing, China
Interests: irrigation scheduling; irrigation control; automatic irrigation; smart irrigation; soil moisture modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiang Li

E-Mail Website
Guest Editor
College of Agricultural Science and Engineering, Hohai University, Nanjing, China
Interests: agricultural water resource allocation; irrigation scheduling; irrigation control; simulation-optimization of irrigation water allocation; irrigation water quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Irrigation scheduling is vital for crop growth in districts with insufficient precipitation, especially in real-time applications. Prompt crop water deficit recognition and water supply control guarantee higher crop yield and better quality. Involved in various methods for irrigation scheduling or crop water management, soil moisture is a key factor, in view of its bridging role, in precisely indicating crop water status and controlling irrigation water supply. Therefore, an investigation of soil moisture sensing or prediction, and how it is acted on reflecting crop water status and further for irrigation management is raised in this Special Issue. Topics include, but are not limited to, the following:

  1. Methods, instruments and systems for soil moisture sensing or water status detection in farmland;
  2. Satellite and UAV remote sensing methods for soil moisture monitoring and generation of irrigation prescription chart;
  3. Data fusion methods for temporal–spatial resolution improvement of soil moisture or water status;
  4. Modelling soil moisture variations for irrigation;
  5. Combined application of crop model and machine learning model for irrigation scheduling based on soil moisture;
  6. Irrigation control methods focused on soil water status;
  7. R&D of efficient irrigation systems based on soil moisture;
  8. Effects of soil structures on soil infiltration and soil water status.

Both original research and review papers are welcome.

Dr. Zhe Gu
Prof. Dr. Jiang Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soil moisture sensing
  • soil moisture prediction
  • irrigation scheduling modelling
  • temporal–spatial resolution
  • irrigation control
  • irrigation system

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Published Papers (1 paper)

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Research

23 pages, 5077 KB  
Article
Evaluating Method-Dependent Estimates of Volumetric Field Capacity in the Roldanillo–Unión–Toro Irrigation District, Colombia
by Harold Tafur-Hermann, Estefania Osorio-Ocampo, Andrés Fernando Echeverri-Sánchez, Edwin Erazo-Mesa and Jhony Armando Benavides-Bolaños
Water 2026, 18(10), 1195; https://doi.org/10.3390/w18101195 - 14 May 2026
Viewed by 666
Abstract
Reliable estimates of volumetric water content at field capacity (θFC) are important inputs for irrigation scheduling because θFC contributes to the estimation of plant-available water, depletion thresholds, and refill targets. In irrigated systems, θFC is therefore an operational decision variable rather than a [...] Read more.
Reliable estimates of volumetric water content at field capacity (θFC) are important inputs for irrigation scheduling because θFC contributes to the estimation of plant-available water, depletion thresholds, and refill targets. In irrigated systems, θFC is therefore an operational decision variable rather than a fixed soil property. However, θFC varies systematically across estimation methods, introducing uncertainty into irrigation management. This study evaluated method-dependent differences in θFC for irrigated tropical soils in the Roldanillo–Unión–Toro agricultural irrigation district (Valle del Cauca, Colombia). Field capacity was estimated at 42 sampling points (0–0.10 m depth) using four methods: Mariotte bottle (MB), filter paper (FP), a pedotransfer function (PTF), and the Richards pressure plate method (RPP). The RPP method was used as an operational reference for comparative purposes, not as an absolute representation of true FC. Agreement and bias were assessed using descriptive statistics, error metrics, regression, Bland–Altman analysis, and texture-stratified comparisons. RPP θFC averaged 39.37% (range: 29.85–46.41%), whereas MB, FP, and PTF produced higher mean values of 42.66%, 44.26%, and 46.38%, respectively. Relative to RPP, mean error and root mean square error increased from MB (3.29% and 5.21%) to FP (4.89% and 8.16%) and PTF (7.01% and 10.82%). Disagreement also varied with soil texture. These results show that low-cost θFC methods are not directly interchangeable with RPP measurements in the evaluated surface layer. Because θFC is commonly used in irrigation calculations, the observed method-dependent differences may affect the estimation of depletion thresholds and refill targets if surface-layer values are extrapolated without local validation. Overall, surface-layer θFC in the Roldanillo–Unión–Toro irrigation district was strongly method-dependent, highlighting the need to account for method-related uncertainty before using alternative θFC estimates as inputs for irrigation decision support. Full article
(This article belongs to the Special Issue Research on Soil Moisture and Irrigation, 2nd Edition)
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