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Agronomy
Special Issues
Remote Sensing Applications in Crop Monitoring and Modelling—2nd Edition
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Remote Sensing Applications in Crop Monitoring and Modelling—2nd Edition

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

Deadline for manuscript submissions: 5 November 2025 | Viewed by 787

Special Issue Editors

Prof. Dr. Chengming Sun

E-Mail Website
Guest Editor
Smart Agriculture Research Institute, Yangzhou University, Yangzhou, 225009, China
Interests: image recognition (computer vision technology) and intelligent monitoring of crop growth; crop growth simulation and its system design; UAV phenotypic monitoring and data analysis; the design and application of agricultural Internet of Things systems
Special Issues, Collections and Topics in MDPI journals
Dr. Minghan Cheng

E-Mail Website
Guest Editor
Agricultural College, Yangzhou University, Yangzhou, 225009, China
Interests: remote sensing in agriculture; machine learning; crop phenotyping; smart agriculture; crop modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate and timely monitoring of crop growth status is essential for smart and sustainable agricultural production. Remote sensing data acquired from various platforms (e.g., satellites, UAVs, and the ground) have been widely used to capture crop growth status at various spatial and temporal scales. In addition, the development of new sensor technologies provides new insights for crop monitoring. Recently, technologies such as multimodal data fusion, crop model assimilation, machine learning, cloud computing, and computer vision have been studied, pertaining to crop growth monitoring, disaster warning, and yield forecasts.

To demonstrate the developments in remote sensing for crop monitoring and modelling, this Special Issue aims to present new and innovative applications of remote sensing data, collected from various platforms and sensors. It also highlights novel mechanisms and data-driven methods, such as data fusion and artificial intelligence, to tackle the issues facing crop production. Topics of interest include but are not limited to the following:

  •     Crop mapping using satellite observations;
  •     Crop growth monitoring using multimodal data fusion;
  •     Cloud computing applications in the remote sensing of agriculture;
  •     High-throughput acquisition of crop phenotypic traits;
  •     Crop biophysical and biochemical parameter retrieval;
  •     Crop yield forecasting;
  •     Crop disaster warning;
  •     Crop model and data assimilation.

Previously, we successfully published a Special Issue titled “Remote Sensing Applications in Crop Monitoring and Modelling”. We now therefore propose a second volume of this Special Issue for a broader range of applications. Scientists from across the world are invited to submit both original research and review articles on these topics.

Prof. Dr. Chengming Sun
Dr. Minghan Cheng
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. Agronomy is an international peer-reviewed open access monthly 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
  • crop monitoring and modelling
  • multimodal data fusion
  • crop phenotype
  • machine learning

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Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 35033 KiB  
Article
Development of Maize Canopy Architecture Indicators Through UAV Multi-Source Data
by Shaolong Zhu, Dongwei Han, Weijun Zhang, Tianle Yang, Zhaosheng Yao, Tao Liu and Chengming Sun
Agronomy 2025, 15(8), 1991; https://doi.org/10.3390/agronomy15081991 - 19 Aug 2025
Abstract
Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four [...] Read more.
Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four planting densities at three different sites, and herein introduce two novel indicators, “kurtosis and skewness,” based on the manually measured leaf area index (LAI) of maize at five different canopy heights. Then, we constructed the LAI, plant height (PH), kurtosis, and skewness estimation models based on unmanned aerial vehicle multispectral, RGB, and laser detecting and ranging data, and further assessed the canopy architecture and estimated yield. The results showed that the fitting coefficient of determination (R2) of cumulative LAI values reached above 0.97, and the R2 of the four indicators’ estimation models based on multi-source data were all above 0.79. A high LAI, along with greater kurtosis and skewness, optimal PH levels, and strong stay-green ability, are essential characteristics of high-yield maize. Moreover, the four indicators demonstrated high accuracy in estimating yield, with the R2 values based on measured canopy indicators at the four planting densities being 0.792, 0.779, 0.796, and 0.865, respectively. Similarly, the R2 values for estimated yield based on estimated canopy indicators were 0.636, 0.688, 0.716, and 0.775, respectively. These findings provide novel insight into maize architecture characteristics that have potential application prospects for efficient estimation of maize yield and the breeding of ideal canopy architecture. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling—2nd Edition)
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17 pages, 5783 KiB  
Article
Analysis of Spatiotemporal Variation and Driving Forces of Vegetation Net Primary Productivity in the North China Plain over the Past Two Decades
by Mingxuan Yi, Dongming Zhang, Zhiyuan An, Kuan Li, Liwen Shang and Kelin Sui
Agronomy 2025, 15(4), 975; https://doi.org/10.3390/agronomy15040975 - 17 Apr 2025
Viewed by 509
Abstract
The net primary productivity (NPP) of vegetation—a critical component of ecosystem carbon cycling and a key indicator of the quality and functionality of ecosystems—is jointly influenced by natural and anthropogenic factors. As NPP is a vital agricultural and ecological region in China, understanding [...] Read more.
The net primary productivity (NPP) of vegetation—a critical component of ecosystem carbon cycling and a key indicator of the quality and functionality of ecosystems—is jointly influenced by natural and anthropogenic factors. As NPP is a vital agricultural and ecological region in China, understanding the spatiotemporal dynamics and driving mechanisms of vegetation NPP in the North China Plain (NCP) has significant implications for regional sustainable development. Utilizing MODIS NPP, temperature, precipitation, and human activity data from 2003 to 2023, this study employs univariate linear regression, ArcGIS spatial analysis, and the Hurst index to investigate the spatiotemporal characteristics, driving factors, and future trends in vegetation NPP. The results indicate that vegetation NPP exhibited a fluctuating upward trend over the 21-year period, with an annual increase of 2.60 g C/m2. Spatially, NPP displayed a “high in the south, low in the north” pattern. There is significant spatial heterogeneity between temperature, precipitation, and vegetation NPP in the study area, with natural factors generally exerting a greater influence than human activities; however, the coupling of human activities with other factors significantly amplify their impact. The Hurst index (mean: 0.43) revealed an anti-persistent future trend in vegetation NPP, suggesting substantial uncertainties regarding its long-term dynamics. These findings enhance our understanding of the responses of vegetation to global change and provide a scientific basis for balancing food security and ecological conservation in the NCP. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Crop Monitoring and Modelling—2nd Edition)
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