Submitted to: Industrial Crops and Products
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2010
Publication Date: 12/21/2010
Citation: Thorp, K.R., Dierig, D.A., French, A.N., Hunsaker, D.J. 2010. Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella. Industrial Crops and Products. 33(2):524-531. Interpretive Summary: Lesquerella seed oil may be used as a biorenewable petroleum substitute in the production of many industrial products, including cosmetics, coatings, plastics, and greases. It also has application as a biorenewable diesel fuel additive. Several issues related to crop management and plant breeding must be resolved before the crop can be produced commercially. In this study, we investigated a remote sensing approach that could be used in management and breeding of lesquerella crops. We demonstrated that remote sensing may be useful for monitoring biomass growth and developmental progression through the flowering stage. Although we implemented expensive hyperspectral instruments to analyze the spectral response of the lesquerella canopy, we also showed how our remote sensing approach could be used to design simpler, inexpensive radiometers for monitoring the crop. The results of this study advance the science of hyperspectral remote sensing for applications in agricultural crop management. Results will benefit plant breeders, growers, and others aiming to develop lesquerella into a commercially viable oilseed crop for production of biorenewable products.
Technical Abstract: Seed oil from lesquerella (Lesquerella fendleri (Gray) Wats.) is currently being developed as a biorenewable petroleum substitute, but several issues related to crop management and breeding must be resolved before the crop will be commercially viable. Due particularly to the prominent yellow flowers exhibited by lesquerella canopies, remote sensing may be a useful tool for monitoring and managing the crop. In this study, we used a hand-held spectroradiometer to measure spectral reflectance over lesquerella canopies in 512 narrow wavebands from 268 to 1095 nm over two growing seasons at Maricopa, Arizona. Biomass samples were also regularly collected and processed to obtain aboveground dry weight, flower counts, and silique counts. Partial least squares regression was used to develop predictive models for estimating the three lesquerella biophysical variables from canopy spectral reflectance. For model fitting and model testing, the root mean squared prediction errors between measured and modeled aboveground dry weight, flower counts, and silique counts were 2.1 and 2.3 Mg ha-1, 251 and 304 flowers, and 1018 and 1019 siliques, respectively. Analysis of partial least squares regression coefficients and loadings highlighted the most sensitive spectral wavebands for estimating each biophysical variable. For example, the flower count model heavily emphasized the reflectance of yellow light at 583 nm, and contrasted that with reflectance in the blue (483 nm) and at the red edge (721 nm). Because of the indeterminate nature of lesquerella flowering patterns, remote sensing methods that monitor flowering progression may aid management decisions related to the timing of irrigations, desiccant application, and crop harvest.