Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella.

摘要

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.