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Remote Sensing
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Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods...
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Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods (Second Edition)

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: 31 October 2026 | Viewed by 1297

Special Issue Editors

Prof. Dr. Renan Falcioni

E-Mail Website
Guest Editor
Department of Biology, State University of Maringá, Maringá 87020-900, Brazil
Interests: plant physiology; remote sensing; hyperspectral data; multivariate statistical analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Renato Herrig Furlanetto

E-Mail Website
Guest Editor
Institute of Food and Agricultural Sciences, University of Florida, Wimauma, FL 33598, USA
Interests: precision agriculture; crop monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Crusiol

E-Mail Website
Guest Editor
Embrapa Soja, National Soybean Research Center, Brazilian Agricultural Research Corporation, Londrina 86085-981, Brazil
Interests: multispectral data; hyperspectral data; thermal imaging system; drought monitoring; spectral data processing; UAV systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to introduce the second edition of the Special Issue of Remote Sensing titled “Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods”.

Precision agriculture using remote sensing techniques is pivotal in optimizing crop yields, managing resources efficiently, minimizing environmental impacts, and diagnosing crop health issues. Recent advancements in sensor technologies and novel data processing methods have unlocked new possibilities for modern agricultural practices. For this Special Issue, we invite researchers to submit original research articles, comprehensive reviews, and insightful case studies focusing on cutting-edge applications of remote sensing technologies in agriculture. We seek contributions that showcase innovative uses of satellite imagery, UAVs (drones), multispectral and hyperspectral sensors, LiDAR, and radar for agricultural tasks such as crop monitoring, soil analysis, pest and disease detection, water resource management, and yield forecasting. We particularly encourage submissions that explore, but are not limited to, the integration of artificial intelligence (AI) and machine learning (ML) in data analysis, and those that discuss the challenges, limitations, and potential of using remote sensing to transform agriculture. Topics of interest include, but are not limited to, the following:

Crop Monitoring and Management: Studies demonstrating advanced techniques for assessing crop growth, health, and development in real time, using remote sensing data to optimize input management and improve yield outcomes.

Soil Analysis: Research on the integration of remote sensing technologies to assess soil properties, moisture levels, nutrient availability, and other parameters critical for soil health and crop productivity.

Pest and Disease Detection: Innovative approaches that utilize remote sensing methods for early detection and monitoring of pests, diseases, and other biotic stresses that affect crop health and yield, with a focus on precision intervention.

Water Resource Management: Applications of remote sensing in monitoring and optimizing irrigation practices, improving water use efficiency, and managing water resources for sustainable agriculture in both rainfed and irrigated systems.

Yield Prediction and Forecasting: Case studies and research focused on the application of remote sensing to model and predict crop yields, helping farmers and policymakers make informed decisions about resource allocation and market trends.

Prof. Dr. Renan Falcioni
Dr. Renato Herrig Furlanetto
Dr. Luis Crusiol
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. 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.

Keywords

  • precision agriculture
  • remote sensing
  • crop monitoring
  • satellite imagery
  • UAVs (drones)
  • multispectral sensing
  • hyperspectral imaging
  • LiDAR in agriculture
  • AI in agriculture
  • sustainable farming

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Related Special Issue

Published Papers (2 papers)

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Research

37 pages, 38849 KB  
Article
Integrating Remote-Sensing Data: UAV Multispectral Imagery, Drone-Derived 3D Canopy Traits and Gridded Climate Variables to Support Potassium Management and Soybean Yield Estimation
by João Vitor Ferreira Gonçalves, Luis Guilherme Teixeira Crusiol, Fabio Alvares de Oliveira, Caio Almeida de Oliveira, Nicole Ghinzelli Vedana, Daiane de Fatima da Silva Haubert, Weslei Augusto Mendonça, Karym Mayara de Oliveira, Thiago Rutz, Renato Herrig Furlanetto, José Alexandre M. Demattê, Roney Berti de Oliveira, Amanda Silveira Reis, Renan Falcioni and Marcos Rafael Nanni
Remote Sens. 2026, 18(7), 1054; https://doi.org/10.3390/rs18071054 - 1 Apr 2026
Viewed by 587
Abstract
This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive [...] Read more.
This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive growing seasons (2022–2023, 2023–2024, and 2024–2025) under different potassium fertilisation strategies and environmental conditions. Machine learning models, particularly the random forest algorithm, were applied to multisource datasets, including UAV-derived canopy structural traits (height and canopy area), spectral indices (NDVI), meteorological variables, and fertilisation information. The foliar K prediction models achieved high accuracy (R2 up to 0.85), while the yield prediction models achieved R2 values between 0.71 and 0.81. The inclusion of the potassium rate and fertilisation strategy further improved model performance, highlighting the strong influence of potassium supply and fertilisation management on plant physiological responses. Interestingly, compared with those required to stabilise grain yield, foliar potassium saturation occurred at substantially higher K2O rates, indicating the occurrence of luxury potassium uptake. The association of UAV-derived canopy metrics with this pattern suggests that remote sensing may help detect subtle nutritional dynamics that are not directly reflected in yield responses. Model interpretability using SHAP analysis identified relationships within the analysed dataset that were consistent with physiological expectations, with positive contributions associated with canopy vigour and negative contributions associated with thermal stress. In addition, probabilistic SHAP analysis provided a decision-oriented perspective by quantifying yield probabilities under contrasting potassium management regimes and climate scenarios. Overall, within the experimental conditions studied, the proposed framework enabled a rapid assessment of crop nutritional status, yield prediction, and the evaluation of fertilisation strategies. The integration of UAV data, climatic variables, and machine learning provides an interpretable basis for potassium management and soybean yield forecasting within the experimental conditions studied, while broader transferability requires external validation. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods (Second Edition))
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27 pages, 7532 KB  
Article
Monitoring Spatiotemporal Dynamics of Soil Moisture Under Water-Nitrogen Interactions in Arid Farmland Using UAV-Based Hyperspectral Sensing and Triple-Band Indices
by Minghui Sun, Kaikai Su and Fei Tian
Remote Sens. 2026, 18(5), 726; https://doi.org/10.3390/rs18050726 - 28 Feb 2026
Viewed by 343
Abstract
In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal [...] Read more.
In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (>80 cm) (R2 = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods (Second Edition))
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