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

Authors: Hatfield, Jerry; Prueger, John; Sauer, Thomas; DOLD, CHRISTIAN - Orise Fellow; O'Brien, Peter; WACHA, KENNETH - Orise Fellow

Submitted to: Inventions
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/4/2019
Publication Date: 12/6/2019
Publication URL: https://handle.nal.usda.gov/10113/6949593
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: Remote sensing has been available for agricultural applications for the past 50 years and over that time there have been many indices developed that relate reflectance to a range of crop canopy properties. These indices use a combination of wavebands and have been used to estimate biomass, leaf area, yield, leaf or crop chlorophyll content, gross primary productivity, crop water stress, and stress from insects and diseases. The linkage of reflectance with thermal infrared temperatures have expanded the utility of these tools to estimate the amount of crop water use. Remote sensing data is now available with drones and systems being operated by individuals. The purpose of this review is to assess the progress that has been made and evaluate where advances can be made in the future by using new tools for analysis of spatial patterns within fields and combine these efforts with artificial intelligence to understand how to use these tools more effectively. This information will be of value to scientists, consultants, and manufacturers of remote sensing systems.

Technical Abstract: Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent 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. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. 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.