Exploring the relationships between ground observations and remotely sensed hazelnut spring phenology

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Phenology

Background:

• Crop phenology is crucial for agricultural monitoring, traditionally relying on field surveys or satellite data.
• Establishing relationships between ground observations and satellite-derived phenology enables large-scale, near-real-time monitoring.
• Hazelnut phenology monitoring using this integrated approach has not been previously explored.

Purpose of the Study:

• To extract phenological metrics from MODIS Enhanced Vegetation Index (EVI) data for hazelnut production regions.
• To compare these satellite-derived metrics with ground-based phenological data (BBCH scale) in Turkey.
• To assess the synchronicity and correlation between remote sensing phenometrics and hazelnut developmental stages.

Main Methods:

• Utilized MODIS EVI data from 2019-2022 in Turkish hazelnut regions.
• Extracted key phenological metrics (Greenup, Upturning Date, Threshold 20%, Start of Season, Peak of Season, Stabilization Date, Maturity).
• Compared extracted metrics with BBCH scale ground phenological data from local orchards.

Main Results:

• Satellite phenometrics like Upturning Date and Threshold 20% correlated significantly with leaf emergence and unfolding.
• Key phenological events, including female flowering and cluster appearance, were temporally aligned with specific EVI curve dynamics.
• Female flowering preceded 20% vegetation development, and cluster appearance aligned with the EVI peak (Stabilization Date).

Conclusions:

• Remote sensing phenometrics derived from MODIS EVI effectively capture hazelnut phenological development.
• The established relationships enable near-real-time, large-scale monitoring of hazelnut crops.
• The methodology is transferable for operational phenology monitoring, with future work considering environmental factors and senescence.