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Agriculture
Special Issues
Remote Sensing in Smart Irrigation Systems
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Remote Sensing in Smart Irrigation Systems

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 586

Special Issue Editors

Prof. Dr. Wenting Han

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: remote sensing; precision agriculture; water use efficiency; smart irrigation; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Dr. Guomin Shao

E-Mail Website
Guest Editor
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
Interests: remote sensing; water use efficiency; evapotranspiration; crop biomass; crop yield

Special Issue Information

Dear Colleagues,

Improving water use efficiency in irrigated agriculture is crucial for sustainable agricultural production to thrive. There is potential to improve water use efficiency through smart irrigation systems, especially with the advent of wireless communication technologies, monitoring systems, and advanced control strategies for optimal irrigation scheduling. With the development of precision irrigation technology in precision agriculture, the use of remote sensing methods, such as UAVs, satellites, ground vehicles, etc., can successfully obtain crop information and monitor soil moisture in agricultural fields quickly, efficiently, and accurately without touching or destroying the original soil structure.

This Special Issue focuses on the effectiveness of remote sensing technology in monitoring water use in agricultural environments to improve the sustainability of crop production. The scope of this Special Issue includes the application of remote sensing technology in irrigation monitoring, the design and optimization of smart irrigation systems, the impact of climate change on irrigation demand, the integration of remote sensing technology with the Internet of Things (IoT), case studies of remote sensing technology applications in different crops and regions, the combination of precision agriculture with remote sensing technology, and the analysis and processing of remote sensing data. We welcome the submission of various types of articles, such as original research papers and reviews.

Prof. Dr. Wenting Han
Dr. Guomin Shao
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Agriculture 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

  • remote sensing
  • smart irrigation
  • water use efficiency
  • soil moisture
  • soil salinity
  • crop biomass
  • machine learning
  • crop health

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Published Papers (1 paper)

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Research

19 pages, 2692 KiB  
Article
Enhanced Spring Wheat Soil Plant Analysis Development (SPAD) Estimation in Hetao Irrigation District: Integrating Leaf Area Index (LAI) Under Variable Irrigation Conditions
by Qiang Wu, Dingyi Hou, Min Xie, Qi Gao, Mengyuan Li, Shuiyuan Hao, Chao Cui, Keke Fan, Yu Zhang and Yongping Zhang
Agriculture 2025, 15(13), 1372; https://doi.org/10.3390/agriculture15131372 - 26 Jun 2025
Viewed by 132
Abstract
Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural [...] Read more.
Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha−1), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R2 increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R2 improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article
(This article belongs to the Special Issue Remote Sensing in Smart Irrigation Systems)
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