Remote Sensing for Crop Production and Management

Dear Colleagues,

Global food security requires the modernization of agriculture, which depends on precision farming with agricultural mechanization, information technology, and biotechnical innovations, through the adoption of genetically-modified cropping systems. Precision agriculture considers the within-field variability of soil, crop growth, and yield, and is implemented in the manner of site-specific management (i.e., instead of managing the entire field as one uniform unit, as in traditional farming), so as to operate at the right time, and at the needed location, with the proper amount of treatments, so as to maximize productivity and minimize costs and environmental pollution.

Remote sensing is such a technology, as it provides data for the prescription of precision agricultural operations with geographic information and global positioning data. Also, remote sensing is a cost-effective measure for the sensing and analysis of all the issues concerned in and around crop fields. Remote sensing data is valuable, in combination with other sources, for improving the estimation of crop parameters. Agricultural remote sensing began in the late 1920s when aerial photography was first used to identify cotton root rot in College Station, TX (Neblette, 1927, Photo-Era Mag. 58:346). Since then, through the general development of remote sensing technology, remote sensing has been developed, inter alia, for crop growth monitoring, estimation of evapotranspiration for irrigation scheduling, nitrogen efficiency analysis, pest management, harvest, and yield estimation. Indeed, in the next decade or so, more specialized remote sensing and analysis techniques for crop production and management will likely be developed and applied toward the improvement of crop production and management.

Review and research papers are invited in, but not limited to, the following topics:

• Remote sensing and precision agriculture
• Remote sensing analysis of climate change for crop proudction
• Remote sensing big data for crop production and management
• New platforms, sensors, and methods for remote crop monitoring
• Satellite remote sensing for crop land observation and classifcation
• Low-altitude remote sensing platforms, including manned airplane and unmanned aerial vehicles (UAV), for crop monitoring, stress detection, and phenotyping
• Ground-based remote sensing platforms, from handheld to on-the-go,  for crop monitoring, stress detection, and phenotyping
• Multispectral vs hyperspectral remote sensing for crop monitoring and stress detection
• Remote sensing for the prescription of variable-rate application
• Assimilation of remote sensing data into crop growth models to improve parameter estimation
• Crop biochemical parameter inversion through physially-based radiative transfer models
• Solar-induced fluorescence signal extrsaction for characterization of crop stress

Dr. Yanbo Huang
Guest Editor

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