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Innovative Remote Sensing Technologies in Precision Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 336

Special Issue Editors


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Guest Editor
Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: agriculture remote sensing; biochemical and biophysical parameters estimation; yield estimation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Agriculture, University of Patras, 27200 Amaliada, Greece
Interests: technologies in soil spectroscopy; innovations and techniques in soil and plant analysis; non-destructive techniques for soil testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in remote sensing technologies have revolutionized precision agriculture by enabling the high-resolution, real-time monitoring of crop and soil conditions. Innovations in satellite, UAV (unmanned aerial vehicle), and ground-based sensor systems have provided farmers with multispectral, hyperspectral, LiDAR, and thermal imaging capabilities, allowing them to assess vegetation health, moisture levels, nutrient deficiencies, and pest and disease infestations with unprecedented accuracy. Artificial intelligence algorithms integrated with these datasets facilitate predictive analytics for yield optimization, irrigation management, and targeted resource application (e.g., fertilizers, pesticides), reducing environmental impacts while maximizing productivity.

This Special Issue welcomes papers on advances in remote sensing for precision agriculture. Topics include, but are not limited to, biochemical and biophysical parameter estimation, soil condition monitoring, yield estimation, pest and disease forecasting, artificial intelligence, novel proximal sensors, and the recent application of multispectral, hyperspectral, LiDAR, SAR, and thermal remote sensing in crop monitoring.

Dr. Weiping Kong
Prof. Dr. Pantelis E. Barouchas
Guest Editors

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Keywords

  • artificial intelligence algorithms
  • yield estimation
  • biochemical and biophysical parameters estimation
  • soil condition monitoring
  • vegetation remote sensing product validation
  • commercial crops, grass, and other crops

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Published Papers (1 paper)

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Research

29 pages, 5144 KB  
Article
A Fully Integrated System: Sentinel-2, Electromagnetic Induction and Laboratory Analyses for Mapping Mediterranean Topsoil Variability
by Alessandra Lepore, Giovanni De Rosa, Elèna Grobler and Giuseppe Celano
Appl. Sci. 2025, 15(21), 11796; https://doi.org/10.3390/app152111796 - 5 Nov 2025
Viewed by 322
Abstract
The accurate characterisation of soil spatial variability is essential for the development of site-specific and sustainable agricultural practices. This study proposes an integrated methodology for effective soil mapping in Mediterranean environments. A preliminary agronomic context assessment (climate and pedology) was followed by electromagnetic [...] Read more.
The accurate characterisation of soil spatial variability is essential for the development of site-specific and sustainable agricultural practices. This study proposes an integrated methodology for effective soil mapping in Mediterranean environments. A preliminary agronomic context assessment (climate and pedology) was followed by electromagnetic induction (EMI) surveying at 14, 7 and 3 kHz. EMI data were processed by ordinary kriging to model spatial structure; the 14 kHz conductivity map—resulting from the frequency most sensitive to topsoil characteristics—was adopted to guide subsequent analysis. Sentinel-2 imagery acquired under bare-soil conditions was screened using the Bare Soil Index (BSI) to confirm vegetation absence, then processed to derive the Clay Index (CI). Guided by the 14 kHz kriged surface, twelve sampling points were selected with ESAP to capture both homogeneous zones and areas of maximum variability. Soil was sampled at 30 cm and analysed for texture, pH, electrical conductivity (ECe) and carbon fractions. CI correlated strongly with apparent electrical conductivity (ECa) (R2 = 0.76; r = 0.87) and showed significant relationships with clay (R2 = 0.69; r = 0.83). The proposed approach provides a robust and scalable alternative to conventional soil mapping, turning routine proximal and satellite data into decision-ready layers for site-specific management. Full article
(This article belongs to the Special Issue Innovative Remote Sensing Technologies in Precision Agriculture)
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