Machine Learning Approaches for Assessing Vegetation Phenology under Climate Change
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".
Deadline for manuscript submissions: closed (6 December 2023) | Viewed by 3289
Special Issue Editors
Interests: UAV remote sensing; machine learning; deep learning; phenology extraction; yield prediction; data fusion
Special Issues, Collections and Topics in MDPI journals
Interests: GNSS data processing and application
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are in the era of climate change, and global warming and irregular precipitation have profoundly influenced vegetation phenology and crop growth, subsequently affecting the carbon balance. Identifying to what extent vegetation phenology has changed and responded to the ongoing climate change will help to understand the inner influencing mechanisms and to provide effective adaptive measures. Therefore, it is essential to explore the changes in vegetation phenology under climate change at multiple scales. With the development of remote sensing technology, the monitoring of vegetation phenology has been significantly improved. In particular, high-resolution satellites and unmanned aerial vehicles (UAVs) have provided convenience for the Earth observation of vegetation changes in phenology.
The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques, such as multi/hyperspectral satellites and UAVs, for monitoring the changes in vegetation phenology under the changing climate. Contributions focusing on new methods and applications in vegetation phenology extraction; the assessment of climate change impacts on vegetation phenology, in particular, new approaches and novel contributions using machine learning; and deep learning methods, specifically studies based on multispectral and hyperspectral from multiple platforms, are welcome. The scope of this Special Issue includes, but is not limited to, the following:
- Vegetation phenology extraction using multi- and hyperspectral images;
- Mapping vegetation phenology;
- Vegetation growth monitoring;
- Time-series analysis monitoring of agriculture and forest;
- High-throughput phenomics;
- Machine learning and deep learning.
Dr. Yahui Guo
Dr. Shunqiang Hu
Guest Editors
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Keywords
- vegetation phenology
- machine learning and deep learning
- high-throughput phenomics
- climate change
- data fusion
- radiometric calibration
- time-series analysis
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