An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations

Abstract

Floral phenology, the timing and intensity of flowering, is intimately tied to the reproduction of terrestrial ecosystem and highly sensitive to climate change. However, observational records of flowering are very sparse, limiting our understanding of spatiotemporal dynamics of floral phenology from local to regional scales. Satellite remote sensing provides unique opportunities to monitor flowers through space and time in a cost-effective way. Here we developed an enhanced bloom index (EBI), based on the multispectral remotely sensed data, to quantify flowering status over almond (Prunus dulcis) orchards in Central Valley of California. Our test studies with unmanned aerial vehicle (UAV) multispectral imagery at 2.6–5.2 cm demonstrated that the EBI enhanced the signals of flowers and reduced the background noise from soil and green vegetation, and agreed well with the bloom coverage derived from supervised classification, with a R2 of 0.72. Experimental tests with multi-scale remote sensing observations from CERES aerial (0.2 m), PlanetScope (3 m), Sentinel-2 (10 m), and Landsat (30 m) satellite imagery further showed the robustness of the EBI in capturing the flower information. We found that the relatively dense time series of PlanetScope and Sentinel-2 imagery were able to capture the bloom dynamics of almond orchards. Satellite derived EBI is expected to track the bloom information and thus improve our understanding and prediction of flower and pollination response to weather and ultimately the yield.