Abstract
Perennial shrub willow crops can simultaneously address environmental issues and produce biomass for biofuels, bioproducts and bioenergy. Chlorophyll is an essential biochemical property for characterizing plant health and growth and remote sensing techniques have been developed to quantify plant chlorophyll concentration. However, these techniques are highly dependent on the ability to obtain a large number of ground-based observations to train and validate the models. Given the species-specific and growth stage-dependent nature of leaf chlorophyll concentration (LCC), and the limited research about LCC estimation for shrub willow, we proposed a new model for quantifying shrub willow LCC from chlorophyll meter (CM) readings. Results indicated that there were statistically significant interaction effects in CM readings between cultivar and root age across willow growth stages. However, neither the cultivar, root age, nor their interaction were statistically significant predictor variables in a model for LCC, so separate response curves for different cultivars or root ages were not necessary. To consider changes in LCC across the growing season, we included growing degree days (GDD) as an additional predictor for estimating LCC from CM readings. The model including GDD performed better (R2 = 0.92, RMSE = 4.81 μg/cm2) than the model (R2 = 0.90, RMSE = 5.38 μg/cm2) using only CM readings. Compared to previous studies, this model reduced complexity and achieved more accurate predictions of seasonal LCC, thus providing a readily applicable approach for mapping willow LCC using remote sensing technologies.
•We developed models to estimate willow chlorophyll concentration from CM readings.•The model incorporating growing degree days considers seasonal variation.•We improved the model performance among willow growth stages.•The model reduced the complexity of field data needed compared to previous studies.•The study supported shrub willow time-series health status monitoring.