Application of Remote Sensing and GIS in Agricultural Engineering
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".
Deadline for manuscript submissions: 15 November 2024 | Viewed by 5054
Special Issue Editors
Interests: remote sensing; data fusion; precision agriculture; digital agriculture; carbon-water-crop nexus; global change
Interests: precision agricultural management systems; UAV; satellite remote sensing
Interests: agricultural robotics; plant phenotyping; machine vision; digital twin; automation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Currently, global food security is increasingly threatened by many uncertainties, such as drought, flood, heat wave, and air pollution. It is well recognized that the traditional means of agricultural production cannot meet the growing demand for high-quality food around the world. Fortunately, precision agriculture management and agricultural engineering applications with remote sensing and GIS provide a hopeful way of capturing crop growth. Recently, many new technologies (e.g., deep learning) and multisource satellite remote sensing data (e.g., Landsat, Sentinel-1/2, and Planet) are drawing more and more attention for practical application in agricultural engineering. In particular, crop growth is largely influenced by the surrounding water and heat conditions, while the carbon in the atmosphere is absorbed through the stage of crop growth. This means that agriculture production is an important bridge connecting carbon and water dynamics across the agroecosystem. Therefore, to advance the understanding of the role of remote sensing and GIS in agricultural engineering, it is necessary to (1) monitor and manage agriculture production using multisource satellite remote sensing images with advanced deep learning algorithms; (2) capture and quantify the carbon and water parameters during agriculture production; and (3) evaluate the impact of different water and heat conditions on agriculture production.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Precision agriculture;
- Digital agriculture;
- Global food security monitoring;
- Fusing multisource satellite remote sensing images;
- Deep learning model application;
- Agricultural engineering.
I/We look forward to receiving your contributions.
Dr. Jiang Chen
Dr. Lorena Nunes Lacerda
Dr. Lirong Xiang
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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
- food security
- multisource satellite remote sensing
- GIS technologies
- deep learning
- agricultural engineering
- agroecosystem management
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