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ARS Home » Pacific West Area » Pendleton, Oregon » Columbia Plateau Conservation Research Center » Research » Publications at this Location » Publication #377668
Research Project: Attaining High Quality Soft White Winter Wheat through Optimal Management of Nitrogen, Residue and Soil Microbes

Location: Columbia Plateau Conservation Research Center

Title: Automated detection of phenological transitions for yellow flowering plants such as brassica oilseeds

Author
item SULIK, JOHN - University Of Guelph
item LONG, DANIEL - Collaborator

Submitted to: Agrosystems, Geosciences & Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/19/2020
Publication Date: 11/26/2020
Citation: Sulik, J.J., Long, D.S. 2020. Automated detection of phenological transitions for yellow flowering plants such as brassica oilseeds. Agrosystems, Geosciences & Environment. 3(1). Article e20225. https://doi.org/10.1002/agg2.20125.
DOI: https://doi.org/10.1002/agg2.20125

Interpretive Summary: Monitoring the growth and development of crops is important for making crop management decisions having to do with crop protection and fertilization. In this study, the onset and duration of flowering of canola was monitored using a sequence of aerial images that were taken over the growing season. At the same time, the crop was characterized with regard to changes in above-ground biomass, timing of flowering, and timing of flower shedding. A computer algorithm was developed that uses a spectral index that is sensitive to the amount of green biomass and one that is sensitive to presense of yellow flowers. Contrasts between these two indices was successfully used to estimate the onset and duration of flowering for a yellow flowering crop such as canola. Changes between vegetative and reproductive development could be automatically detected, which lends itself to detection of flowering within and between farm fields using sensors mounted on satellites or aircraft. Such information can be integrated with agrometeorological data to assess disease risk and yield prediction.

Technical Abstract: Monitoring crop phenology is crutial for making site-specific management decisions for crop protection and nutrition. The prominent yellow bloom assciiated with canola (Brassica napus) and similar yellow-flowering plants can provide clues about spatial differences as well as timing of crop input requirements. The objective of this study was to remotely characterize the phenological development of Brassicaceae oilseeds such as canola and carinata (B. carinata) in terms of spectral-temporal dynamics between vegetation density and yellow flower density. Temporal variation of spectral indices (NDVI, NDYI, and VARI) were measured in small plots over the growing season in relation to changes in vegetation density and flower density in winter canola and spring carinata. Phenological changes between vegetative and reproductive development could be automatically detected using the difference in change of the sign of delta-Index values between VARI and NDYI. An overall accuracy of 85% was obtained when testing the algorithm with Landsat 8 data of canola fields near Olds, Alberta, Canada. A bivariate time series analysis procedure was developed for automatically estimating flowering transitions based on predictable, relative differences between vegetative and reproductive indices. Bivariate temporal analysis can adequately characterize oilseed phenology transitions during low levels of exposed soil and prominent bloom. Researchers and land managers can exploit optimal phenology windows to improve site-specific models and disease risk assessments.