Construction and analysis of Airborne Hyperspectral orthophoto data set in the whole growth cycle of cotton

Cotton is the main economic crop in China, but there is still a lack of high-quality monitoring sample datasets for research on low-altitude remote sensing fine monitoring. Hyperspectral images not only have rich spectral and spatial characteristics, but also are widely used in biochemical parameter inversion, plant diseases and insect pests monitoring, growth potential assessment, and yield prediction. To this end, this paper builds an airborne hyperspectral image dataset for the full growth cycle of cotton (AHS-FGCC). Based on the DJI M600 Pro drone equipped with a Rikola hyperspectral imager, a total of 7 phases(seedling, seedling-seeding, bud, florescence, boll-forming, peak bolling and open-boll periods) of UAV hyperspectral data of the same cotton varieties in the same area with a height of 100 m. This data has been standardly preprocessed. Referring to the Geographic Information Metadata specification, the airborne hyperspectral cotton metadata information is supplemented to build a full growth cycle cotton hyperspectral orthography image dataset. The results show that the reflectance curves obtained by the Rikola imaging spectrometer and the ASD ground object spectrometer have good consistency in the wavelength range of 503-850 nm. In addition, the three typical spectral features of "green peak feature", "red valley feature" and "red edge feature" are basically consistent. This dataset can better reflect the spectral characteristics of cotton in different growth periods and can provide sample data for fine monitoring of cotton by low-altitude remote sensing, and provide a reference for related research on the construction of crop hyperspectral or multispectral datasets.