Methods of classification and mapping of the land cover using satellite monitoring data have recently been frequently applied to solve practical tasks in digital agriculture, including refining field boundaries and identifying unused lands. This paper discusses the recognition of arable lands using Sentinel-2 satellite images. Images with and without atmospheric correction were utilized for classifying five types of underlying surfaces in the Oktyabrsky and Leninsky districts of the Jewish Autonomous Region. Various machine learning methods and software tools were applied for image classification. It was determined that the overall classification accuracy for images with atmospheric correction exceeded 80%, which is significantly higher than the corresponding rate for uncorrected images. The obtained results were used to prepare shapefiles outlining agricultural fields in the Jewish Autonomous Region in 2022. The proposed approach can be applied to refine field boundaries at the regional level without the preparation and processing of time series of satellite images, which require substantial time and computational resources.

Artem N. Polyakov
Postgraduate Student
Khabarovsk Federal Research Center of the Far Eastern Branch
of the Russian Academy of Sciences
54, Dzerzhinsky Str., Khabarovsk, 680000, Russia
Engineer
Computing Center of the Far Eastern Branch
of the Russian Academy of Sciences
65, Kim Yu Chen Str., Khabarovsk, 680063, Russia
e-mail: artem_polyakov@inbox.ru
ORCID: 0009-0007-9526-5114

Alexey S. Stepanov
Doctor of Pharmaceutical Sciences
Leading Researcher
Far Eastern Agricultural Research Institute
13, Klubnaya Str., Vostochnoe, kr. Khabarovskiy, 680521, Russia
e-mail: stepanfx@mail.ru
ORCID: 0000-0001-8395-8350
Web of Science ResearcherID: AAY-7208-2020
Scopus AuthorID: 7402419500
AuthorID: 247904

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