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Applied Sciences
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Advanced Satellite Image Processing for Agricultural and Environmental Monitoring
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Advanced Satellite Image Processing for Agricultural and Environmental Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (10 December 2025) | Viewed by 1947

Special Issue Editors

Dr. Nathalie dos Santos Guimarães

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: smart farming; crop monitoring; precision agriculture; remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Helder José Chaves Fraga

E-Mail Website
Guest Editor
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building, and Sustainability of Agri-Food Production (Inov4Agro), Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: applied climatology; mediterranean crops; climate change; crop modelling; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite remote sensing has become an essential tool for monitoring agricultural systems and environmental dynamics at multiple spatial and temporal scales. With the increasing availability of high-resolution, multispectral, hyperspectral, and radar satellite imagery, researchers and practitioners can now access unprecedented volumes of data to observe crop development, land use changes, soil moisture variability, deforestation, droughts, and other climate-related phenomena.

This Special Issue aims to explore recent advances in satellite image processing techniques applied to agricultural and environmental monitoring. It seeks to gather cutting-edge contributions that employ modern computational approaches such as machine learning, deep learning, and data fusion, as well as physics-based modelling and multi-temporal change detection. Special emphasis will be placed on studies that demonstrate practical applications related to sustainable agriculture, food security, climate change adaptation, biodiversity monitoring, and natural resource management.

We welcome original research articles, technical developments, and comprehensive reviews that focus on, but are not limited to, the following topics:

  • Crop classification, growth monitoring, and yield prediction using satellite imagery;
  • Detection and assessment of extreme weather events and their impacts on agriculture;
  • Integration of satellite data with in situ or UAV observations;
  • Time-series analysis for land cover and land use change detection;
  • AI-enhanced feature extraction and segmentation in remote sensing;
  • Soil health and vegetation indices derived from multi-sensor satellite data;
  • Climate-smart agriculture through Earth observation technologies.

This Special Issue provides a platform for researchers, professionals, and policymakers to share innovative methods and applications that enhance the use of satellite-based Earth observation for monitoring agricultural systems and environmental sustainability in the face of a changing climate.

Dr. Nathalie dos Santos Guimarães
Dr. Helder José Chaves Fraga
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. Applied Sciences 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 2400 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
  • machine learning
  • deep learning
  • crop monitoring
  • image processing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 5630 KB  
Article
A New Method for Detecting Plastic-Mulched Land Using GF-2 Imagery
by Shixian Lu, Shuyuan Zheng, Cheng Chen, Shanshan Liu, Jian Dao, Chenwei Xu and Jianxiong Wang
Appl. Sci. 2025, 15(22), 11978; https://doi.org/10.3390/app152211978 - 11 Nov 2025
Viewed by 411
Abstract
Plastic mulch residues threaten soil fertility and contribute to microplastic pollution, creating an urgent need for accurate, rapid mapping of plastic-mulched land (PML). This study presents a novel method for detecting PML from GF-2 imagery by introducing the second component of the K-T [...] Read more.
Plastic mulch residues threaten soil fertility and contribute to microplastic pollution, creating an urgent need for accurate, rapid mapping of plastic-mulched land (PML). This study presents a novel method for detecting PML from GF-2 imagery by introducing the second component of the K-T transform as a PML-enhancement feature to compensate for the sensor’s limited spectral bands. The K-T component was fused with selected texture metrics and the original spectral bands, and an object-oriented classification framework was applied to delineate PML. Validation shows that the proposed method achieves high identification accuracy for PML and good transferability, with accuracies exceeding 90% across the four selected study areas. Moreover, the method demonstrates strong temporal stability: classification accuracies exceeded 90% for two different time periods within the same study area. Compared with methods reported in previous studies, our approach attains comparable accuracy while offering higher classification efficiency. Overall, the proposed method enables accurate PML identification from GF-2 imagery and provides a valuable reference for agricultural planning and ecological protection. Full article
(This article belongs to the Special Issue Advanced Satellite Image Processing for Agricultural and Environmental Monitoring)
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20 pages, 3788 KB  
Article
A Simple Aridity Index to Monitor Vineyard Health: Evaluating the De Martonne Index in the Iberian Peninsula
by Nazaret Crespo-Cotrina, Luís Pádua, André M. Claro, André Fonseca, Francisco J. Rebollo, Francisco J. Moral, Luis L. Paniagua, Abelardo García-Martín, João A. Santos and Helder Fraga
Appl. Sci. 2025, 15(19), 10605; https://doi.org/10.3390/app151910605 - 30 Sep 2025
Viewed by 1259
Abstract
Viticulture in the Iberian Peninsula is increasingly threatened by climate change, particularly rising temperatures and prolonged droughts. This study evaluates the ability of the De Martonne Index (DMI), a simple climatic aridity index based solely on temperature and precipitation, to serve as a [...] Read more.
Viticulture in the Iberian Peninsula is increasingly threatened by climate change, particularly rising temperatures and prolonged droughts. This study evaluates the ability of the De Martonne Index (DMI), a simple climatic aridity index based solely on temperature and precipitation, to serve as a proxy for vineyard health over a 30-year period (1993–2022). Vineyard health was assessed using the Vegetation Health Index (VHI), derived from satellite remote sensing data. DMI values were computed from bias-corrected ERA5-Land data, and VHI composites were generated from NOAA satellite imagery. Vineyard-specific outputs were isolated using land cover datasets, and a contingency analysis compared drought classifications from both indices. Results show a strong spatio-temporal correspondence between low DMI values and reduced VHI, with agreement rates for severe/extreme drought conditions reaching up to 56% under the most restrictive DMI thresholds. In the analyzed period, years such as 1995, 1997, 2005, 2009, and 2012, showed over 20% of vineyard areas affected by moderate-to-severe/extreme drought. The spatial analysis revealed that northern and northwestern regions of the peninsula experienced less drought stress, while central and southern areas were more frequently affected. This approach demonstrates that the DMI alone can provide a reliable assessment of vineyard health, potentially enabling its direct use with seasonal forecasts, which are generally available for temperature and precipitation, to anticipate drought impacts and support adaptation in viticulture. The proposed methodology is scalable and transferable to other crops and regions, serving as a tool for climate adaptation strategies in viticulture. Full article
(This article belongs to the Special Issue Advanced Satellite Image Processing for Agricultural and Environmental Monitoring)
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