Estimating Productivity Measures in Guayule Using UAS Imagery and Sentinel-2 Satellite Data

Guayule (Parthenium argentatum Gray) is a perennial desert shrub currently under investigation as viable commercial alternative to the Pará rubber tree (Hevea brasiliensis), traditional source of natural rubber. Previous studies on guayule have shown close association between morphological traits or biomass and content. We collected multispectral RGB-derived Structure-from-motion (SfM) data using an unmanned aircraft system (UAS; drone) determine if incorporating both high-resolution normalized difference vegetation index (NDVI; indicator plant health) canopy height (CH) information could support model predictions crop productivity. Ground-truth resource allocation in was measured at four elevations (i.e., tiers) along crop’s vertical profile measurement techniques novel volumetric technique. Multiple linear regression models estimating fresh weight (FW), dry (DW), volume (FV), fresh-weight-density (FWD), dry-weight-density (DWD) were developed their performance compared. Of productivity measures considered, predicting FWD material adjusted by its freshly harvested volume) NDVI, CH, NDVI:CH interaction, tier parameters reported lowest mean absolute percentage error (MAPE) field measurements predictions, ranging from 9 13%. A reduced only NDVI explore scalability medium spatial resolutions with Sentinel-2 satellite data. Across all UAS surveys corresponding imagery compared, MAPE for below 3% irrespective soil pixel influence.