Machine Learning Matching of Sentinel-2 and GPS Combine Harvester Data to Estimate Within-Field Wheat Grain Yield
by: Joel Segarra, Jose Luis Araus and Shawn C. Kefauver
This study aimed to find the most suitable data combination and estimation model of within-field durum wheat (Triticum durum) grain yield using Sentinel-2. The study was conducted in Spain, as one of the top European producers. Within-field grain yield data was obtained from a GPS combine harvester machine for 7 fields in 2018, which were consecutively processed to match Sentinel-2 10 m pixel size. Vegetation indices NDVI and GNDVI as well as biophysical parameters LAI and FAPAR were calculated from Sentinel-2 bands using the SNAP Sentinel2 ToolBox. Besides those, the Sentinel-2 10 m resolution spectral bands were used as variables for modeling, including multilinear, random forest and support vector machine regression.