Crop Phenology Modelling Using Proximal and Satellite Sensor Data

Understanding crop phenology is crucial for predicting crop yields and identifying potential
risks to food security. The objective was to investigate the effectiveness of satellite sensor
data, compared to field observations and proximal sensing, in detecting crop phenological stages.
Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed
during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP),
Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent.
Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation)
demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover),
with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR
showed decreasing variability as the growing season progressed. The use of a double sigmoid
model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid)
and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure
and the maximum values to flowering and fruit development. Furthermore, increasing the frequency
of sensor revisits is beneficial for detecting short-duration crop phenological stages. The
results have implications for data assimilation to improve crop yield forecasting and agri-environmental
modeling.