Data presented here include imagery with ground-sampling distances of 3 cm and 7 cm for March 2019, May 2019 and July 2019. Also included are the corresponding ground-truth training and verification data presented as shapefiles, as well as the classification output and other data relevant to the project such as the width of floral units. The imagery was acquired by Spectrum Aviation using A6D-100c (50mm) Hasselblad cameras with bayer filters, mounted on a Sky Arrow 650 manned aircraft. Ground-truth data for training maximum likelihood classifications and for verifying the accuracy of classifications were gathered within eight days of imagery acquisition. Ground-truth data were acquired from sown field margins and hedgerow surrounding one study field. This dataset was acquired from March to July 2019 at a farm in Northamptonshire, UK. Data were acquired as part of a NERC funded iCASE PhD studentship (NERC grant NE/N014472/1) based at the University of East Anglia and in collaboration with Hutchinsons Ltd. The aim of the research was to map the floral units of five nectar-rich flowering plant species using very high resolution multispectral imagery. Each species constitutes an important food resource for pollinators. The plant species in question were Prunus spinosa, Crataegus monogyna, Silene dioica, Centaurea nigra and Rubus fruticosus. Full details about this dataset can be found at https://doi.org/10.5285/cf68be0c-e969-4190-8ec6-abeedb51b42c

This dataset contains RGB photographs acquired from drone surveys. There are 741 harvest plots from 38 surveys at 36 sites around the world. Each site was approximately 1 ha in area. Included with the photographic images are the coordinates of ground control markers, biomass, taxonomic and location data for harvest plots and ancillary metadata. The observations can be used to obtain allometric size-biomass models. This work was supported by the Natural Environment Research Council award number NE/R00062X/1 as part of the project 'Do dryland ecosystems control variability and recent trends in the land CO2 sink?' Full details about this dataset can be found at https://doi.org/10.5285/1ec13364-cbc6-4ab5-a147-45a103853424