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Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: a review

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2017 ASABE Annual International Meeting  1700272.(doi:10.13031/aim.201700272)
Authors:   Joshua D Rudd, Gary T Roberson, John J Classen
Keywords:   agricultural aviation, hyperspectral imagery, multispectral imagery, remote sensing, satellite imagery.


Data resources for precision agriculture applications are expanding. Image capturing satellite constellations are growing in number, unmanned aircraft systems (UAS) are seeing widespread use as they become more powerful and easier to use, and ground-based units are becoming less expensive to produce. Moreover, the different sensors types that can be used with these three platforms are increasing in variety and capability. Although, the three different remote sensing platforms can supply some similar types of data, each system delivers some unique capabilities suitable for specific uses. This review seeks to demonstrate the potential benefits and shortcomings of data gathered from satellites, UAS, and ground sensors, and how they can be used for different applications. Satellite data can be obtained at varying spatial and temporal resolutions, but the data is easily corrupted by cloud cover. It can also be several days between usable flight paths. Small UAS provide flexibility regarding sensor types and flight timings, and they produce imagery at a higher spatial resolution. These platforms have a wide range in cost and most cannot be used during rain or high wind; moreover, the data quality can be influenced by light conditions. Ground sensors can produce ultra-high resolution data that is less affected by environmental conditions but gathering the data is labor intensive and time consuming. Out of the three systems, UAS are the most versatile, but there are circumstances where data from the other two is more suitable for specific applications.

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