Leveraging Satellite Earth Observation for Detecting Bloom Shifts and Phenological Patterns in California’s Almond Orchards

Given the impact of climate change on deciduous crop yields, our research focuses on leveraging earth observation remote sensing to accurately detect flowering periods in almond orchards and evaluate a climate-based dormancy model for predicting flowering times. This study addresses the challenge of monitoring almond flowering phenology by employing automated crop mapping techniques to support phenology monitoring across California's Central Valley. Using Sentinel-2 (S2) multispectral satellite imagery, we compare its effectiveness with the carbohydrate-temperature (C-T) dormancy model. The study area encompasses approximately 30,000 almond orchards, precisely identified using the Almond Industry Map. We utilized time-series analyses of the Enhanced Bloom Index (EBI) and the Normalized Difference Vegetation Index (NDVI) to quantify bloom periods and intensity and determine peak bloom times. Leveraging around 4,000 S2 tiles, enhanced vegetation indices, and in situ time-lapse camera data collected from 2019 to 2022, we developed a robust methodology for accurately identifying peak bloom periods. This process created a comprehensive phenological dataset, which was standardized and interpolated to daily resolution for improved time-series analysis. Our approach achieved a mean absolute error (MAE) of just 1.9 days in detecting peak bloom, demonstrating the accuracy of satellite-based phenological monitoring. This underscores both the advantages and limitations of remote sensing technologies in agricultural phenology. The dataset was then used to validate projections from the climate-based carbohydrate-temperature (C-T) dormancy model, offering valuable insights and supporting the refinement of this mechanistic approach. The study revealed significant spatial and temporal patterns in flowering phenology, emphasizing the role of regional climatic conditions in influencing crop development. Results highlight the potential of remote sensing and satellite imagery to detect the start, peak, and end of bloom in almond orchards with high precision, generate valuable phenological datasets, monitor patterns at both regional and field scales, and assess the reliability of dormancy models. This research has critical implications for improving agricultural practices and supporting decision-making in the almond industry. By advancing phenological monitoring techniques, our study presents a scalable and innovative approach to managing perennial crops in the face of climate change.