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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #369751
Research Project: Managing Energy and Carbon Fluxes to Optimize Agroecosystem Productivity and Resilience

Location: Soil, Water & Air Resources Research

Title: Applications of vegetative indices from remote sensing to agriculture: past and future.

Author
item Hatfield, Jerry
item Prueger, John
item Sauer, Thomas
item DOLD, CHRISTIAN - Orise Fellow
item O'Brien, Peter
item WACHA, KENNETH - Orise Fellow

Submitted to: Inventions
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2019
Publication Date: 12/6/2019
Citation: Hatfield, J.L., Prueger, J.H., Sauer, T.J., Dold, C., O'Brien, P.L., Wacha, K.M. 2019. Applications of vegetative indices from remote sensing to agriculture: past and future. Inventions. 4(4):71. https://doi.org/10.3390/inventions4040071.
DOI: https://doi.org/10.3390/inventions4040071

Interpretive Summary:

Technical Abstract: Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality.