New Progress on Remote Sensing Technology and Its Application in Agriculture
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 June 2025 | Viewed by 1178
Special Issue Editors
Interests: remote sensing; data fusion; radiative transfer modelling; machine learning; yield prediction; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: crop yield estimation; crop model data assimilation; drought monitoring; causal inference; causal effect analysis
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of vegetation productivity and evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Global agriculture faces increasing demands for productivity and sustainability, and remote sensing technology offers critical tools for addressing these challenges. Traditional agricultural monitoring methods rely on limited data sources, often lacking the spatial, temporal, and spectral richness needed for accurate assessments. This single-source approach constrains precision, making it difficult to capture dynamic crop conditions and environmental variability effectively. The integration of multisource data fusion, radiative transfer models, and artificial intelligence (AI) will advance sustainable agriculture. Multisource data fusion combines satellite, UAV, and ground-based data, allowing for the more comprehensive and accurate monitoring of crop conditions, soil moisture, and environmental variables. Radiative transfer models enhance our understanding of the interaction between electromagnetic radiation and vegetation, enabling the precise retrieval of crop characteristics. AI, including machine learning and deep learning, plays a critical role in analyzing complex datasets, facilitating crop stress detection, yield prediction, and disease monitoring. Together, these technologies provide actionable insights for precision agriculture, resource optimization, and climate resilience, supporting sustainable farming practices globally. This Special Issue requests research on innovative approaches and applications, fostering collaboration across disciplines to address key agricultural challenges.
Dr. Jiang Chen
Dr. Wen Zhuo
Prof. Dr. Rui Sun
Guest Editors
Manuscript Submission Information
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Keywords
- multisource data fusion
- radiative transfer models
- artificial intelligence
- data assimilation
- sustainable agriculture
- precision agriculture
- digital agriculture
- crop monitoring
- climate resilience
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