Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, the coloration of flowers is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signal is usually limited to in-situ observations at a flower scale. Modeling flowering dynamics (e.g., blooming, span, spatial distribution) at the landscape scale may reveal hints on ecological processes, diversity of plants and pollinators, and phenological adaptations to environmental changes. Multitemporal hyperspectral aerial and satellite-based observations are especially suited for such efforts, as they can be sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signal using as a case study an time series of imagery from the airborne imaging spectrometer AVIRIS-NG collected as part of the SBG High-Frequency Time Series (SHIFT) campaign. Flowering and non-flowering plants were sampled weekly between late February and May 2022 over two natural reserve areas in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels. Physically-based radiative transfer models (RTM) were used on the AVIRIS-NG data to investigate the spectro-temporal variation and spatial distribution of flowering species against non-flowering ones. Observed flowering spectral features were characterized by weak blue absorptions and gradient temporal variations within the green and red spectral range. Inverse fitting of RTM parameters of pigment contents (e.g., carotenoids, anthocyanins) and plant structural traits (e.g., LAI) led to the advance of a model able to account for flower pigments absorptions. Our approach for mapping flowering events from modeling spectro-temporal dynamics opens opportunities for future satellite monitoring of floral cycles at broader scales.