Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data

Guayule (Parthenium argentatum, A. Gray), a perennial desert shrub, produces high-quality natural rubber and is targeted as a domestic natural rubber source in the U.S. While commercialization efforts for guayule are on-going, crop management requires plant growth monitoring, irrigation requirement assessment, and final yield estimation. Such assistance for guayule management could be provided with remote sensing (RS) data. In this study, field and RS data, collected via drones, from a 2-year guayule irrigation experiment conducted at Maricopa, Arizona were evaluated. In-season field measurements included fractional canopy cover (f_c), basal (K_cb) and single (K_c) crop coefficients, and final yields of dry biomass (DB), rubber (RY), and resin (ReY). The objectives of this paper were to compare vegetations indices from MS data (NDVI) and RGB data (triangular greenness index, TGI); and derive linear prediction models for estimating f_c, K_cb, K_c, and yield as functions of the MS and RGB indices. The NDVI and TGI showed similar seasonal trends and were correlated at a coefficient of determination (r²) of 0.52 and a root mean square error (RMSE) of 0.11. The prediction of measured f_c as a linear function of NDVI (r² = 0.90) was better than by TGI (r² = 0.50). In contrast to TGI, the measured f_c was highly correlated with estimated f_c based on RGB image evaluation (r² = 0.96). Linear models of K_cb and K_c, developed over the two years of guayule growth, had similar r² values vs NDVI (r² = 0.46 and 0.41, respectively) and vs TGI (r² = 0.48 and 0.40, respectively). Final DB, RY, and ReY were predicted by both NDVI (r² = 0.75, 0.53, and 0.70, respectively) and TGI (r² = 0.72, 0.48, and 0.65, respectively). The RS-based models enable estimation of irrigation requirements and yields in guayule production fields in the U.S.