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Recent Progress in GIS and Remote Sensing for Agriculture-Related Applications (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2117

Special Issue Editors

Geography and Urban Sustainability Department, United Arab Emirates University, Al Ain P.O. Box No. 15551, United Arab Emirates
Interests: geoinformation science; flood hazard; crop mapping; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; agro-geoinformatics; environmental modeling; geospatial information interoperability and standards; cyberinfrastructure; digital twin; AI/machine learning; image processing and analysis; pattern recognition; crop mapping
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Spatial Information Science and Systems, George Mason University, 4400 University Drive, MSN 6E1, George Mason University, Fairfax, VA 22030, USA
Interests: earth system science; geospatial information science; agro-geoinformatics; geospatial web service; spatial data infrastructure; geospatial data catalog; interoperability standard; agricultural drought monitoring and forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring agricultural activities can provide valuable information to farmers, investors, and decision makers. In recent years, space-based crop identification and monitoring have played a significant role in agricultural studies due to the development of remote sensing and GIS technology. Moreover, recent progress in digital technologies, such as cloud computing, data fusion, deep learning, artificial intelligence, social media, and cyber-infrastructure, has enhanced the spatial and temporal scale of crop mapping with new tools and methods for facilitating and promoting new innovative approaches in agriculture. This Special Issue will enable the publication of papers that explore the progress in innovative crop mapping and monitoring research and applications.

Papers suitable for this Special Issue must address relevant topics in agricultural mapping and monitoring and include sound implementation and validation procedures. We welcome submissions that describe state-of-the-art approaches in the progress in GIS and remote sensing for agriculture-related applications, including, but not limited to, the following:

  • Research on remote sensing crop mapping theory, methodology, and practices;
  • Geospatial information for stratification and sampling;
  • Data fusion, calibration, validation, and ground truths for agricultural monitoring;
  • Crop mapping, condition monitoring, acreage estimation, and yield modeling;
  • Cropland evapotranspiration, soil moisture, and drought monitoring and assessment;
  • Remote sensing-based agricultural disaster monitoring, assessment, mitigation, and emergency response;
  • Global climate and environmental change and its impacts on agriculture sustainability and food security;
  • Remote sensing monitoring and modeling on agricultural greenhouse gases;
  • Agricultural environment and public health;
  • Crop/plant disease detection, monitoring, and assessment;
  • Cloud computing and big data in agriculture-related applications;
  • Agricultural land use and land cover change;
  • Agricultural deep learning and artificial intelligence technology;
  • Analysis of spatial data uncertainty.

Dr. Li Lin
Dr. Chen Zhang
Prof. Dr. Liping Di
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. 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

  • remote sensing
  • GIS
  • crop mapping
  • crop modeling
  • crop yield estimation
  • data processing
  • agricultural monitoring
  • AI and machine learning
  • spatial–temporal analysis

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Published Papers (1 paper)

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Review

15 pages, 1418 KiB  
Review
Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management
by Bhashitha Konara, Manokararajah Krishnapillai and Lakshman Galagedara
Remote Sens. 2024, 16(23), 4514; https://doi.org/10.3390/rs16234514 - 2 Dec 2024
Cited by 1 | Viewed by 1565
Abstract
Crop nitrogen (N) management in agricultural fields is crucial in preventing various environmental and socio-economic issues arising from excess N use. However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) [...] Read more.
Crop nitrogen (N) management in agricultural fields is crucial in preventing various environmental and socio-economic issues arising from excess N use. However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) offers a promising approach to overcoming these challenges, and numerous studies have explored its application in N management. This review aims to analyze research trends in applying DIP for N management over the past 5 years, summarize the most recent studies, and identify challenges and opportunities. Web of Science, Scopus, IEEE Xplore, and Engineering Village were referred to for literature searches. A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. In addition, image data using more variables as model inputs, including agriculture sensors and meteorological data, have increased prediction accuracy. Nonetheless, several challenges associated with DIP, including obtaining high-quality datasets, complex image processing steps, costly infrastructure, and a user-unfriendly technical environment, still need to be addressed. Full article
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