Cropland and Yield Mapping with Multi-source Remote Sensing
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: 20 October 2024 | Viewed by 200
Special Issue Editors
Interests: agricultural models; data assimilation; crop yield estimation
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; meteorology; agriculture; environment; climatology
Special Issues, Collections and Topics in MDPI journals
Interests: crop growth monitoring; yield estimation and prediction; multi-source remote sensing data fusion
Interests: spatial big data technology; machine learning; deep learning; crop mapping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Accurate and timely information on cropland distribution and crop yield estimation or in-season forecasting can be used to support government agricultural decision making, assist in agricultural management practices, and optimize resource use. With the rapid development of the radiometric, spatial, temporal, and spectral resolutions of remote sensing technology, the integration of multi-source remote sensing is a good way to enhance the spatial resolution, improve data accuracy, capture a broader range of environmental variables, and enable the comprehensive monitoring and analysis of landscapes at various scales. Therefore, to better understand the challenges and opportunities presented by integrating multi-source remotely sensed observations for agricultural applications (especially for cropland or crop yield mapping), this Special Issue aims to invite original and innovative research on applications of multi-source remote sensing for croplands, the crop yield, and crop-type mapping, or crop parameter retrieval using leveraging data assimilation algorithms, machine learning, and deep learning methods, or other state-of-the-art approaches. The research areas may include (but are not limited to) the following:
- Crop yield estimation or forecasting;
- Farmland or crop-type mapping;
- Multi-sensor imagery fusion;
- Spatially explicit crop model development, implementation, and validation;
- Model data assimilation algorithms, systems, and uncertainty;
- Multi-source data for retrieving crop parameters;
- Machine learning or deep learning for agricultural studies.
Dr. Wen Zhuo
Prof. Dr. Shibo Fang
Prof. Dr. Yi Xie
Dr. Xiaochuang Yao
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
- yield mapping
- crop yield estimation or forecasts
- cropland mapping
- crop parameter retrieval
- multi-source imagery
- crop growth model
- Google Earth Engine
- data assimilation
- machine learning
- deep learning
