Conference paper Open Access

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

Sitokonstantinou Vasileios; Koukos Alkiviadis; Kontoes Charalampos; Bartsotas Nikolaos; Karathanassi Vassilia


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    <subfield code="a">cotton phenology ,agricultural insurance, semi-supervised learning, early warning, pseudo labels</subfield>
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    <subfield code="a">This work has been supported by the e-shape project, which has been funded by the European Union's Horizon 2020 innovation programme under grant agreement 820852.</subfield>
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    <subfield code="d">11-16 July 2021</subfield>
    <subfield code="a">2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS</subfield>
    <subfield code="c">Brussels, Belgium</subfield>
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    <subfield code="u">National Observatory of Athens</subfield>
    <subfield code="a">Koukos Alkiviadis</subfield>
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    <subfield code="u">National Observatory of Athens</subfield>
    <subfield code="a">Kontoes Charalampos</subfield>
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    <subfield code="u">National Observatory of Athens</subfield>
    <subfield code="a">Bartsotas Nikolaos</subfield>
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    <subfield code="u">National Technical University of Athens</subfield>
    <subfield code="a">Karathanassi Vassilia</subfield>
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    <subfield code="u">https://ieeexplore.ieee.org/abstract/document/9553456</subfield>
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    <subfield code="c">2021-10-12</subfield>
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    <subfield code="u">National Observatory of Athens</subfield>
    <subfield code="a">Sitokonstantinou Vasileios</subfield>
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    <subfield code="a">Semi-Supervised Phenology Estimation in Cotton Parcels with Sentinel-2 Time-Series</subfield>
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    <subfield code="c">820852</subfield>
    <subfield code="a">EuroGEO Showcases: Applications Powered by Europe</subfield>
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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
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    <subfield code="a">10.1109/IGARSS47720.2021.9553456</subfield>
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