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Ground, Proximal and Remote Sensing for Precision Agriculture Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 792

Special Issue Editors

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Guest Editor
Institute of Atmospheric Pollution Research (CNR-IIA), National Research Council of Italy, Monterotondo, RM, Italy
Interests: geomatics; UAV; aircraft and satellite remote sensing; multispectral and hyperspectral remote sensing; imaging spectroscopy; precision agriculture; infrastructures analysis by remote sensing techniques; photogrammetry and 3D modelling; GIS and geospatial statistics; CAL/VAL; land use land cover change; development of multi-parametric approaches for environmental monitoring
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Guest Editor
Department of Urban Planning and Spatial Information, Feng-Chia University, Taichung 407802, Taiwan
Interests: geographic vegetation analysis by remote sensing techniques; precision agriculture and forestry; information science; aerial survey; digital elevation model technologies and applications; quantitative analysis of watershed geomorphology; environmental planning and management; geospatial statistics; remote sensing

Guest Editor
CREA—Council for Research in Agriculture and Economics Research Centre for Agriculture and Environment, 50125 Florence, Italy
Interests: remote and proximal soil sensing; digital soil mapping; spectroscopy; gamma-ray radiometrics; geomatics; soil monitoring; soil survey; soil biodiversity; ecological networks; ecosystem modelling

Special Issue Information

Dear Colleagues,

Precision Agriculture (PA) is one of the most prominent and increasing segments in the agricultural economic area. Nevertheless, most of criticalities for small companies are referred to their deficiency to access to the newest technologies. That’s why, the adoption of specific and low-cost technologies could strongly promote a sustainable and economical management of medium and small agricultural systems. The soil composition variability, microclimate and lighting conditions may differ within each Production Unit and affect vegetative vigor and the physiological parameters of plants, with direct effects on qualitative and quantitative production. It is known that the PA encompasses a wide number of methodologies that monitor different aspects of soils, plant vigor, or the vegetative–productive balance. These localized inspections allow to obtain site-specific characteristics and to program specifical approaches. In the last decade, there has been a progressive diffusion of Unmanned Aerial Systems (UAS) technologies for PA purposes, as rapid and accurate methodologies designed to evaluate the spatial variability of geometric plant parameters and plant vigor analysis by means of commercial and Open Source (OS) software. Considering the significant market increase in UAS in agricultural applications, the need for such kind of technologies in the agricultural sector could generate a great economic impact. Such tendence is evident by the reinforcement of the scientific compound focusing on topics related to UASs in precision agriculture compounds. In addition to UAS and satellite platforms, the application of different ground sensing technologies (such as spectroscopy and Unmanned Ground Vehicles (UGVs)) could reinforce the capacities to analyze and extract specific information by computing, for example, site-specific time-series analysis and low-cost Decision Support Systems (DSS).

The aim of this Special Issue is to collect papers that present innovative studies, sensors, or technologies for the improvement of PA and PF (Precision Forestry) via the use of combined Ground, Proximal and Remote sensing technologies. The integration of the above technologies in the immediate future should simplify management and promote carbon farming and agroforestry practices simultaneously. Considering the need to reduce procedural budgets and enhance the accuracy of maps, papers that use OS software and cost-effective technologies will be more than welcomed. For cost-efficient technologies data fusion can be considered by integrating different spatial, spectral, and radiometric data resolutions.

The scope of this Special Issue, regarding the combination or single use of ground, proximal and remote sensing in PA and PF, includes, but is not limited to, the following:

  • Soil digital mapping;    
  • Canopy condition assessment;
  • Canopy architecture computation;
  • Missing plants detection;
  • Biophysical plant parameters;
  • Spatio-temporal analysis;
  • Geostatistics of ground sensing;
  • Decision support system;
  • Carbon farming.

Article types: research articles, review papers, communication papers, and technical notes.

Dr. Alessandro Mei
Dr. Yung-Chung Chuang
Dr. Carlos Lozano Fondón
Guest Editors

Roberto Barbetti
Guest Editor Assistant
Email: [email protected]
CREA—Council for Research in Agriculture and Economics Research Centre for Forestry and Wood, 0142 Casale Monferrato, Italy
Interests: soil survey and applied pedology; digital soil mapping; soil monitoring on the go proximal soil sensors; soil spectroscopy and soil spectral library (SSL); digital soil assessment of agricultural suitability; precision farming and precision forestry

Manuscript Submission Information

Manuscripts should be submitted online at 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. Remote Sensing 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 2700 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.


  • satellite remote sensing
  • UAS and UGV
  • digital soil mapping
  • ground sensor network
  • vegetation analysis and yield estimation
  • precision agriculture and forestry
  • image processing and pattern recognition
  • field and laboratory Spectroscopy
  • artificial intelligence
  • decision support systems
  • geographical information systems
  • geostatistics

Published Papers (1 paper)

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26 pages, 9900 KiB  
Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics
by Yiming Guo, Shiyu Jiang, Huiling Miao, Zhenghua Song, Junru Yu, Song Guo and Qingrui Chang
Remote Sens. 2024, 16(12), 2133; - 13 Jun 2024
Viewed by 228
Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra [...] Read more.
Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management. Full article
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