**2. Materials and Methods**

We evaluated cross-platform data products and methods to estimate ET in a high-yield commercial winegrape vineyard. We used small Unmanned Aerial Systems (sUAS) to capture high-spatiotemporal, multispectral imagery to compare to well-established EOS Landsat 8 (https://landsat.gsfc.nasa.gov/landsat-8/) and Sentinel 2 (https://sentinel.esa.int/web/sentinel/ missions/sentinel-2) systems. We timed our sUAS field campaigns to coincide with EOS overpasses. We used EOS imagery to calculate ET using both a reflectance based NDVI approach and the emerging EEFlux approach. For EOS ET estimates, Sentinel 2 Bottom of Atmosphere reflectance imagery was used for the OpenET NDVI model and Landsat 8 was used for EEFlux. sUAS imagery was calibrated, stitched, and georeferenced to produce reflectance and structure-from-motion (SfM) surface model products. The OpenET NDVI model approach [31] was used to generate high resolution sUAS-based ET estimates from reflectance, and surface models were used to identify canopy and ground pixels to mask soil in subsequent analysis. As single date LiDAR collection was used to validate SfM canopy models. Each platform's raster-based ET estimates were resampled to have consistent spatial origin, extent, resolution and index to track coincident pixel value and change throughout the season and across data source. We used a combination of Ardupilot Mission Planner (v.1.3.59), Pix4D Pix4DMapper (v.4.3.3), Phoenix LiDAR Systems SpatialFuser (v.3.5.1) and CloudCompare (v.2.11 alpha) for sUAS data collection and manipulation; ESA Snap Toolbox (v.6.0), OSGeo GDAL (v.2.3.2), Google Earth Engine Python API (v.0.1.217), Python (v.3.6) and ESRI ArcMap (v.10.5.1) for EOS data manipulation; and ESRI ArcGIS Pro (v.2.4) and R (v.3.5.3) for platform comparison and analysis.
