<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The assumption of the European Union Common Agricultural Policy is to maintain good agricultural practices for sustainability in the environment. A number of requirements are imposed on farmers, including the maintenance of permanent grassland, fallow land or crop diversification. To meet these requirements, the European Union guarantees subsidies, but at the same time fields must be monitored focusing on crop identification. The limitation of field inspection and substituting it with crop recognition using satellite images could increase the effectiveness of this procedure. The application of satellite imagery in automatic detection and identification of dominant crops over a large area seems to be technically and economically sound. The paper discusses the concept and the results of automatic classification based on a Random Forests classifier performed on multitemporal images of Sentinel-2 and Landsat-8. A test site was established in a complex agricultural structure with long and narrow parcels in the south-eastern part of Poland. Time-series images acquired during the growing season 2016 were used for multispectral classification in different configurations: for Sentinel-2 and Landsat-8 separately and for both sensors integrated. Different Random Forests approaches and post-processing methods were examined based on independent data from farmers’ declarations records, reaching the best accuracy of over 90% for crops like winter or spring cereals. Overall accuracy of the classification ranged from 72% to 91% depending on the classification variant. The elaborated scheme is novel in the context of Polish complex agricultural structure and smallholders.</p></abstract>

The assumption of the European Union Common Agricultural Policy is to maintain good agricultural practices for sustainability in the environment. A number of requirements are imposed on farmers, including the maintenance of permanent grassland, fallow land or crop diversification. To meet these requirements, the European Union guarantees subsidies, but at the same time fields must be monitored focusing on crop identification. The limitation of field inspection and substituting it with crop recognition using satellite images could increase the effectiveness of this procedure. The application of satellite imagery in automatic detection and identification of dominant crops over a large area seems to be technically and economically sound. The paper discusses the concept and the results of automatic classification based on a Random Forests classifier performed on multitemporal images of Sentinel-2 and Landsat-8. A test site was established in a complex agricultural structure with long and narrow parcels in the south-eastern part of Poland. Time-series images acquired during the growing season 2016 were used for multispectral classification in different configurations: for Sentinel-2 and Landsat-8 separately and for both sensors integrated. Different Random Forests approaches and post-processing methods were examined based on independent data from farmers’ declarations records, reaching the best accuracy of over 90% for crops like winter or spring cereals. Overall accuracy of the classification ranged from 72% to 91% depending on the classification variant. The elaborated scheme is novel in the context of Polish complex agricultural structure and smallholders.

Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images

Artificial Satellites

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images"}

The assumption of the European Union Common Agricultural Policy is to maintain good agricultural practices for sustainability in the environment. A number of...

Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images

Publié en ligne: 31 déc. 2020

Pages: 185 - 208

Reçu: 21 mai 2020

Accepté: 18 déc. 2020

DOI: https://doi.org/10.2478/arsa-2020-0013

Mots clés
Crop identification, multitemporal classification, Random Forests, Sentinel-2, Landsat-8, greening practices

© 2020 Joanna Pluto-Kossakowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images

Publié en ligne: 31 déc. 2020

Pages: 185 - 208

Reçu: 21 mai 2020

Accepté: 18 déc. 2020

DOI: https://doi.org/10.2478/arsa-2020-0013

Mots clésCrop identification, multitemporal classification, Random Forests, Sentinel-2, Landsat-8, greening practices

© 2020 Joanna Pluto-Kossakowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Crop identification, multitemporal classification, Random Forests, Sentinel-2, Landsat-8, greening practices