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Abstract

Summary

In this article, satellite images of Landsat-8 and terrestrial imagery were used as a source of information for the automated recognition of winter wheat sowing fields. The land of the Lichanka territorial community, Kyiv-Svyatoshinsky district of Kyiv region was selected as a research object. During the work, a correlation was established between the brightness values of terrestrial snapshots RGB of winter wheat, obtained from terrestrial field surveys, with the pixel brightness values of remote sensing images of different spectral channels and NDVI values. The most significant correlation coefficients of magnitude (RGB) with the values of brightness are as follows, r: channel 5 - 0.77, channel 6 - 0.69, channel 7 - 0.53, channel 10 - 0.74, channel 11-0, 59, NDVI - 0.91. Given the significant correlation between magnitudes (RGB) and NDVI, high informativeness of the 5th channel near infrared (0.845–0.885 μm), chosen to build the identification model should be considered natural. The use of the developed model of culture recognition allowed to territorially identifi winter wheat crops and distinguish them from other vegetation. The results of comparing the model’s identification ability and the NDVI’s vegetation index indicate its reliability and the feasibility of further improvement.

EAGE Publications BV © 2020 The Authors © European Association of Geoscientists and Engineers
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/content/papers/10.3997/2214-4609.202056019
2020-11-10
2024-04-25

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References

  1. Bargiel, D.A
    [2017] New method for crop classification combining time series of radar images and crop phenology information.Remote Sens. Environ.,198, 369–383. (in Germany).
     [Google Scholar]
  2. Cakharova, Ye.YU., Sladkikh, L.A., Kulik, Ye.N.
    [2016] Identifikatsiya sel’skokhozyaystvennykh kul’tur na osnove ispol’zovaniya dannykh distantsionnogo zondirovaniya zemli.Journal Interékspo heo-Sybir’,1–4. (in Russian).
     [Google Scholar]
  3. Durgun, Y.O., Gobin, A., Van De Kerchove, R., Tychon, B.
    [2016] Crop area mapping using 100-m Proba-V time series. Remote Sens. Environ
     [Google Scholar]
  4. Ghazaryan, G., Dubovyk, O., Low, F., Lavreniuk, M., Kolotii, A., Schellberg, J., & Kussul, N.
    [2018] A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics.European Journal of Remote Sensing,51:1, 511–524. (in Ukrainian).
     [Google Scholar]
  5. Hentze, K., Thonfeld, F., & Menz, G.
    [2016] Evaluating crop area mapping from MODIS time-series as an assessment tool for Zimbabwe’s “fast track land reform programme”. Plos One,11(6). (in Germany).
     [Google Scholar]
  6. Li, Q., Wang, C., Zhang, B., Lu, L.
    [2015] Object-based crop classification with Landsat-MODIS enhanced time-series data.Remote Sens. (in China).
     [Google Scholar]
  7. Liu, J.; Zhu, W.; Atzberger, C.; Zhao, A.; Pan, Y.; Huang, X.
    [2018] A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data.Remote Sens.,10, 1203. (in China).
     [Google Scholar]
  8. Moskalenko, A. A.
    [2017] Identifikatsiya osnovannykh medonosnykh kul’tur i danimi a.Textbook Zemleustriy, kadastr i monitorynh zemel’, 2, 66–73. (in Ukrainian).
     [Google Scholar]
  9. Publichna kadastrova karta Ukrainy [n.d.] URL: https://map.land.gov.ua.
     [Google Scholar]
  10. Sun, C., Bian, Y., Zhou, T., Pan, J.
    , [2019] Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. National Center for Biotechnology Information, (in China).
     [Google Scholar]
  11. Trofymenko, P., Trofimenko, N., Veremeenko, S., Furmanets, O.
    [2019] Remote monitoring of winter crops development using the satellite data.18th International Conference on Geoinformatics - Theoretical and Applied Aspects.
     [Google Scholar]
  12. Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., Rudiger, C., Strauss, P.
    [2018] Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study.Remote Sens.
     [Google Scholar]
  13. Waldner, F., Canto, G.S., & Defourny, P.
    [2015] Automated annual cropland mapping using knowledge-based temporal features.ISPRS Journal of Photogrammetry and Remote Sensing,110, 1–13.
     [Google Scholar]
  14. Wu, D.; Yu, Q.; Lu, C.; Hengsdijk, H.
    [2006] Quantifying production potentials of winter wheat in the North China Plain.Eur. J. Agron.,24, 226–235. (in China).
     [Google Scholar]
  15. Zheng, Y.; Zhang, M.; Zhang, X.; Zeng, H.; Wu, B.
    [2016] Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products.Remote Sens.,8, 824. (in China)
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202056019
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Remote identification of winter wheat crops in monitoring tasks
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202056019
/content/papers/10.3997/2214-4609.202056019
/content/papers/10.3997/2214-4609.202056019
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