Remote sensing of rice crop areas with UAVs data: Krasnodar region, Russia

Anatoliy V. Pogorelov; Evgeniy N. Kiselev; Evgeniy S. Boyko; Viacheslav V. Krylenko

doi:10.1117/12.2570635

Translator Disclaimer

26 August 2020 Remote sensing of rice crop areas with UAVs data: Krasnodar region, Russia

Anatoliy V. Pogorelov, Evgeniy N. Kiselev, Evgeniy S. Boyko, Viacheslav V. Krylenko

Author Affiliations +

Proceedings Volume 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020); 1152402 (2020) https://doi.org/10.1117/12.2570635
Event: Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 2020, Paphos, Cyprus

Abstract

The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budget Scientific Institution "Federal Scientific Center of Rice" with an area of 327 hectares. The main cultivated crop is the Flagman rice variety. The survey was performed by a quadcopter with a MicaSense RedEdge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite data covering the entire analyzed period (06/05/2019 - 08/29/2019). To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9 All-Russian Research Institute of Rice (“VNII Rice”). Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

Citation Download Citation

Anatoliy V. Pogorelov, Evgeniy N. Kiselev, Evgeniy S. Boyko, and Viacheslav V. Krylenko "Remote sensing of rice crop areas with UAVs data: Krasnodar region, Russia", Proc. SPIE 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 1152402 (26 August 2020); https://doi.org/10.1117/12.2570635

ACCESS THE FULL ARTICLE