Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.4
Journals
Agronomy
Special Issues
Vegetation Remote Sensing for Sustainable Water and Nutrient Management in...
share announcement

Vegetation Remote Sensing for Sustainable Water and Nutrient Management in Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 1061

Special Issue Editors

Dr. Francisco Montoya

E-Mail Website
Guest Editor
Regional Centre of Water Research (UCLM), Ctra. Las Peñas km. 3,2, 02071 Albacete, Spain
Interests: remote sensing; crop water use; water use efficiency; energy balance; cover crop; water and nutrient combined use
Dr. Susana Ferreira

E-Mail Website
Guest Editor
National Institute for Agrarian and Veterinary Research (INIAV), I.P., Estrada de Leiria, 2460-059 Alcobaça, Portugal
Interests: precision agriculture; remote sensing; crop evapotranspiration; farmland management; water management

Special Issue Information

Dear Colleagues,

The sustainable use of water and nutrients is a central challenge for modern agriculture, particularly within the current framework of the EU Common Agricultural Policy (CAP), which emphasizes resource efficiency, soil protection, and reductions in environmental impacts. This Special Issue will focus on advances in remote and proximal sensing technologies for assessing, monitoring, and optimizing crop water and nutrient use at the farmland scale. We welcome original research and review articles that introduce innovative and operative methodologies, including spectral indices, thermal sensing, fluorescence/chlorophyll measurements, LiDAR, data-fusion approaches, and machine learning techniques for diagnosing water and nutrient stress, canopy development, and plant traits. Studies integrating sensing data with crop modelling, fertilization and irrigation scheduling, decision-support systems, or ground-based observations are particularly encouraged. Contributions addressing macro-nutrient (such as nitrogen or phosphorus) management, nutrient use efficiency, and their combined interactions with water availability will be highly valued. By linking technological developments to practical applications, this Special Issue aims to support resilient, productive, and environmentally responsible agroecosystems.

Dr. Francisco Montoya
Dr. Susana Ferreira
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. Agronomy 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

  • crop water use
  • deficit irrigation strategies
  • nutrient use efficiency
  • nutrient stress
  • nutrient management strategies
  • data fusion
  • machine learning
  • precision agriculture

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 4838 KB  
Article
Retrieving Soil Water Content in Winter Wheat Fields Using UAV-Based Multi-Source Remote Sensing and Machine Learning
by Yanhong Que, Dongli Wu, Mingliang Jiang, Jie Deng, Cong Liu, Su Wu, Fengbo Li and Yanpeng Li
Agronomy 2026, 16(7), 717; https://doi.org/10.3390/agronomy16070717 - 30 Mar 2026
Viewed by 447
Abstract
Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an [...] Read more.
Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R2) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems. Full article
(This article belongs to the Special Issue Vegetation Remote Sensing for Sustainable Water and Nutrient Management in Agriculture)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop