Semi-Supervised Phenology Estimation in Cotton Parcels with Sentinel-2 Time-Series

This study presents a dynamic phenology stage estimation methodology for cotton towards early warning and mitigation advice against natural disasters. First, a time-series comparison algorithm, based on Earth Observation (EO) data, is used to assign pseudo-labels to approximately 1,000 parcels. For this, we employ only a limited number of ground truth samples. The pseudo-labels are then used to train Random Forest (RF) regression models for phenology stage estimation. The pseudo-labeling process is used to augment the annotated dataset and allow for modelling the growth of cotton. The models are applied and evaluated on two different test sites in Greece; for which field campaigns were carried out to collect the labels. The results are satisfactory and showcase the successful generalization of the models to other areas. The dynamic predictions for cotton growth and extreme weather events, from numerical weather prediction (NWP) models, are invaluable information for decision-making relevant to agricultural insurance schemes and farm management.