Deciphering the spectra of flowers to map landscape‐scale blooming dynamics

Abstract Like leaves, floral coloration 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 signals is usually limited to in situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multi‐temporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) collected as part of the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data were used to investigate flowering species' spectro‐temporal variation and spatial distribution using spectral mixture residual (SMR), Gaussian clustering techniques, and a proposed narrow‐band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high‐variance endmembers (leaves, flowers, soil, and dark) and low‐variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro‐temporal dynamics throughout the season, from pre‐blooming to post‐flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red‐Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species ( Coreopsis gigantea , Artemisia californica ) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.