Crop Quantitative Monitoring with Remote Sensing II
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: 31 January 2025 | Viewed by 8536
Special Issue Editors
Interests: crop growth simulation; precision agriculture; vegetation parameter retrieval; remote sensing assimilation
Special Issues, Collections and Topics in MDPI journals
Interests: crop type mapping; crop yield forecasting; data assimilation; crop growth models
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; data mining; data assimilation
Special Issues, Collections and Topics in MDPI journals
Interests: LiDAR application in vegetation; vegetation parameter retrieval; vegetation monitoring; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Crop quantitative monitoring is important to decision support in crop production management practices for sustainable agricultural development and global food security. Today, remote sensing has been extensively used to monitor agricultural fields for crop field mapping, crop phenology, crop disaster stress, real-time crop yield estimation or forecasting, and so on. Various advanced quantitative algorithms have been developed for improved crop classification (e.g., long-term and high-resolution crop maps for wheat, maize, and rice), as well as time series for crop phenology detection and critical crop parameter retrieval (e.g., leaf area index retrieval from canopy radiative transfer model), crop disaster monitoring (drought, flooding, lodging, pests, and diseases), and so on. Applications can be at the global, national, regional, farm or field level, such as county-level yield prediction under climate change and agricultural emissions, which a combination of quantitative remote sensing and crop growth models can carry out.
Dr. Tiecheng Bai
Prof. Dr. Jianxi Huang
Dr. Qingling Wu
Prof. Dr. Wei Su
Guest Editors
Manuscript Submission Information
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Keywords
- quantitative remote sensing
- time series analysis
- crop growth models
- data assimilation
- machine learning
- deep learning
- climate change
- crop parameter retrieval
- crop growth monitoring
- crop stress monitoring
- crop disaster monitoring
- crop phenology detection
- crop type mapping
- crop yield estimation or forecasting
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Related Special Issue
- Crop Quantitative Monitoring with Remote Sensing in Remote Sensing (8 articles)