Remote Sensing Applications in Crop Monitoring and Modelling
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 80
Special Issue Editors
Interests: remote sensing in agriculture; machine learning; crop phenotyping; smart agriculture; crop modeling
Interests: image recognition (computer vision technology) and intelligent monitoring of crop growth; crop growth simulation and its system design; UAV phenotypic monitoring and data analysis; the design and application of agricultural Internet of Things systems
Special Issue Information
Dear Colleagues,
The accurate and timely monitoring of crop growth status is essential to smart and sustainable agricultural production. Remote sensing data acquired from various platforms (e.g., satellite, UAV, and ground) have been widely used to capture crop growth status at various spatial and temporal scales. In addition, the development of new sensor technologies provides new insights for crop monitoring. Recently, technologies such as multimodel data fusion, crop model assimilation, machine learning, cloud computing, and computer vision have been studied, pertaining to crop growth monitoring, disaster warning, and yield forecast.
To demonstrate the developments in remote sensing for crop monitoring and modeling, this Special Issue aims to present new and innovative applications of remote sensing data, collected from various platforms and sensors, as well as highlight novel mechanisms and data-driven methods, such as data fusion and artificial intelligence, to tackle the issues facing crop production. Topics of interest include, but are not limited to, the following:
- Crop mapping using satellite observations;
- Crop growth monitoring using multimodel data fusion;
- Cloud computing applications in the remote sensing of agriculture;
- High-throughput acquisition of crop phenotypic traits;
- Crop biophysical and biochemical parameter retrieval;
- Crop yield forecasting;
- Crop disaster warning;
- Crop model and data assimilation.
Dr. Minghan Cheng
Prof. Dr. Chengming Sun
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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
- crop monitoring and modeling
- multimodal data fusion
- crop phenotype
- machine learning
