Remote Sensing for Crop Nutrients and Related Traits
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: closed (31 August 2024) | Viewed by 20617
Special Issue Editors
Interests: crop monitoring; hyperspectral remote sensing; spectra-based nutrient management
Interests: plant phenotyping; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The acquisition of plant traits and nutritional information is crucial for precision crop nutrient management. Imaging spectroscopy is one of the most widely used remote sensing technologies in precision crop nutrient management, due to its advantage of estimating crops’ nutritional status and soil nutrient availability in a rapid and non-destructive manner. However, the application of imaging spectroscopy in crop nutrient monitoring remains challenging, given the fact that multiple nutrients (macro- and micronutrients), stresses, and phenological stages may have similar spectral responses confounded at different temporal and spatial scales. Therefore, it is critical to have a comprehensive understanding of the potential and limitations of imaging spectroscopy in crop monitoring so that we may further develop imaging spectroscopy in conjunction with other agro-technologies (e.g., mechanical engineering, variable rate technology, UAVs, UGVs, robotics, crop modeling, and AI) for a more precise characterization of plant traits, nutritional status, and even the causes of crop stresses.
This Special Issue aims to collect studies (both review and research articles) on the monitoring of crop nutrients and their related traits. Topics may cover a broad sense of applications of imaging spectroscopy methods and remote sensing data (e.g., multispectral, hyperspectral, thermal, fluorescence, and LiDAR), data analysis techniques (vegetation index, image analysis, radiative transfer modeling, machine learning, and deep learning) for the remote monitoring of crop nutritional status, and other issues of relevance. Articles may address, but are not limited to, the following topics:
- UAV remote sensing
- satellite remote sensing
- crop nutrients
- biomass
- leaf pigments
- leaf area index
- crop water stress
- spectra-based crop nutrient management
Prof. Dr. Fei Li
Prof. Dr. Kang Yu
Guest Editors
Manuscript Submission Information
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Keywords
- plant imaging spectroscopy
- crop nutritional traits
- vegetative index
- machine learning
- hyperspectral
- multispectral
- thermal
- fluorescence
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