About this Research Topic
Crop monitoring using multisource satellite and unmanned aerial vehicle remote sensing leverages data from various platforms to enhance agricultural productivity and sustainability. This approach combines high-resolution imagery from UAV and extensive spatial coverage from satellites, which include both optical and Synthetic Aperture Radar (SAR) data. Satellites provide broad, continuous monitoring of crop health, growth, and yield at large scales, whereas UAVs offer finer details and flexibility, capturing specific field-level information such as plant height, canopy structure, and disease signs. Integrating these multisource datasets improves the accuracy and timeliness of crop assessments, aiding in early detection of stressors like drought, pests, and nutrient deficiencies. The combined approach supports precision agriculture, optimizing resource usage and reducing environmental impact. As climate change intensifies, these technologies play a critical role in sustainable farming practices by enabling data-driven decisions, enhancing food security, and promoting sustainable agricultural systems.
A main challenge in crop monitoring is effectively fusing diverse remote sensing data to improve crop growth and health assessments. Integrating satellite optical and SAR data is crucial, as optical remote sensing provides detailed information about vegetation growth and health through reflected sunlight, while SAR, which can penetrate clouds and capture data under all-weather conditions, offers insights into the structure of crops and soil moisture. This fusion of optical and SAR data enhances consistency and observation frequency in crop monitoring. Additionally, fusing satellite and UAV data is essential. Satellites offer large-scale, regular coverage, but their resolution may miss fine details, whereas UAVs provide high-resolution insights but on a limited scale. Fusing these datasets can enable more precise, field-level monitoring across broad areas. To achieve this, the research topic should focus on developing robust fusion algorithms and frameworks that integrate these data sources, improving methods for real-time monitoring and precision management in agriculture science.
This Research Topic welcomes original research articles and reviews. Research areas may include, but are not limited to, the following:
1. Advanced algorithms for fusing optical, SAR, and UAV data in crop monitoring;
2. Comparative study of optical and SAR data fusion for crop monitoring;
3. Scaling UAV data to satellite imagery for detailed field-level monitoring;
4. Integrating SAR and optical data for crop structure and soil moisture analysis;
5. Real-time crop monitoring frameworks using multisource data.
A main challenge in crop monitoring is effectively fusing diverse remote sensing data to improve crop growth and health assessments. Integrating satellite optical and SAR data is crucial, as optical remote sensing provides detailed information about vegetation growth and health through reflected sunlight, while SAR, which can penetrate clouds and capture data under all-weather conditions, offers insights into the structure of crops and soil moisture. This fusion of optical and SAR data enhances consistency and observation frequency in crop monitoring. Additionally, fusing satellite and UAV data is essential. Satellites offer large-scale, regular coverage, but their resolution may miss fine details, whereas UAVs provide high-resolution insights but on a limited scale. Fusing these datasets can enable more precise, field-level monitoring across broad areas. To achieve this, the research topic should focus on developing robust fusion algorithms and frameworks that integrate these data sources, improving methods for real-time monitoring and precision management in agriculture science.
This Research Topic welcomes original research articles and reviews. Research areas may include, but are not limited to, the following:
1. Advanced algorithms for fusing optical, SAR, and UAV data in crop monitoring;
2. Comparative study of optical and SAR data fusion for crop monitoring;
3. Scaling UAV data to satellite imagery for detailed field-level monitoring;
4. Integrating SAR and optical data for crop structure and soil moisture analysis;
5. Real-time crop monitoring frameworks using multisource data.
Keywords: digital agriculture, precision agriculture, crop monitoring, satellite, UAV, remote sensing
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