Climate shifts, especially warmer winters, reduce deciduous tree-crops sustainability because commercially productive plantations require chill. The newly developed Carbohydrate-Temperature (C-T) model establishes a metabolic association between carbohydrate metabolism in dormant trees, hourly wither temperature and predicting blooming time based on the model. The C-T model was applied in Google Earth Engine (GEE) to predict the blooming time based on PRISM temperature data for almond orchards in the Central Valley of California from 2017-to 2022. Then, a time-sires analysis was applied to two spectral indexes of an enhanced blooming index (EBI), quantifying flowering status and Normalized difference vegetation index (NDVI) quantifying Chlorophyte status over almond orchards to validate the C-T model. The EBI model based on satellite data was validated based on drones and time-lapse cameras to assess the blooming phenology of flowering and leafing. The temporal and spatial extents were used to label satellite image pixels with the proportion of a pixel footprint that is flowering. The model accurately predicted pixel-level flowering proportion throughout the flowering season, across sites with dense to the sparse canopy and different background soil covers, and is robust to not detecting false positive flowering when no flowering events occur. The model can be deployed to generate regional maps of flowering dynamics and can be used to monitor deciduous tree-crops conditions and phenology. In addition, the C-T model was validated and parametrized, enabling predicting blooming time based on the hourly temperature data at the regional scale. The research breaches the gap between fundamental science and farming applications and could guide post-harvest farming applications.
Dr. Tarin Paz-Kagan
Incorporation of Winter Tree Physiology into Satellite-Based Rorecast Models of Orchards Bloom and Yield
Volcani Institute, Agricultural Research Organization, Israel