Biological-based and remote sensing techniques to link vegetative and reproductive development and assess pollen emission in Mediterranean grasses

Connecting the signals of the vegetative and reproductive cycles of plants using large-scale phenological techniques is not always an easy task, and this complexity increases considerably when analysing the plant life cycle in grasses, due to the ubiquity and diversity of this taxonomic family. This work integrates remote sensing techniques (NDVI from satellite remote sensing data and greenness from near-surface imagery) and biological-based techniques (airborne pollen monitoring and field observations and sampling) to analyse phenological patterns and productivity in grass-dominated vegetation types. We aim to answer two main applied and unanswered questions; i) how are the specific phases of vegetative and reproductive cycles in grasses linked at the species and plant community level? and ii) which grass-dominant habitats are the major contributors of grass pollen emission to the atmosphere at the plant community level? The multi-scale integration and validation of large-scale methods such as satellite remote sensing data and aerobiological monitoring using high-resolution or field phenological techniques is recommended. The results clearly support the hypothesis that the highest rates of grass pollen emission are successively produced when the major grass-dominated vegetation types go through the final phases of vegetative development during their biological senescence or equivalent phases. At the plant community level, natural and semi-natural grass-dominated vegetation types, rather than grass cropland habitats, constitute the major sources of pollen emission. The major contributors to the grass pollen emission at the species level are also identified. Finally, a positive relationship between year-to-year primary productivity (measured as annual sum or maximum NDVI) and pollen production (measured as airborne pollen intensity) was observed at the community level. This is a very timely study, as the availability of remote sensing data is increasing interest in generating enhanced forecasting model of allergenic airborne pollen.