Analysis of information technology of satellite monitoring of the development of agricultural crops using fuzzy logic

Authors

  • Kavats Olena
  • Kavats Yurii
  • Hyrka Anatolii
  • Kibukevych Julia

DOI:

https://doi.org/10.34185/1562-9945-5-154-2024-08

Keywords:

satellite monitoring, Landsat-8, Sentinel-1, Sentinel-2, crop yield, fuzzy logic, radar data, crop development, weather conditions.

Abstract

Climate change is increasingly affecting agricultural production in European countries, and modern satellite data methods offer practical solutions for monitoring the development of crops and subsequently predicting their yields. Crop production, which is largely dependent on natural re-sources and weather conditions, constantly faces such dangerous phenomena as sharp changes in air temperature and moisture regime during the growing season, which directly affect crop produc-tivity. The article investigates the impact of hydrothermal conditions on the development of agri-cultural plants using satellite monitoring. Data from Landsat-8, Sentinel-1, and Sentinel-2 satellites allow us to identify the peculiarities of plant development in crops and threats to crop productivity. Studies have confirmed that the use of fuzzy logic helps in planning and optimizing crop production in the face of climate change.

References

S.Yaghobi, Heidarizadi Z., Mirzapour H. "Comparing NDVI and RVI for forest density estima-tion and their relationships withrainfall." Environmental Resources Research (2019) : Vol. 7, No. 2.

Roberto Filgueiras. Crop NDVI Monitoring Based on Sentinel 1/Roberto Filgueiras, Everardo Chartuni Mantovani, Daniel Althoff, Elpídio Inácio Fernandes Filho, Fernando França da Cunha//Remote Sens. –11, 1441, 2019.

Dinesh Kumar. Radar Vegetation Index as an Alternative to NDVI for Monitoring of Soyabean and Cotton/Dinesh Kumar, Srinivasa Rao and Jaswant RAJ Sharma.// Indian Cartographer. – Vol. XXXIII, 2013.

Alejandro Mestre-Quereda. Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping/Alejandro Mestre-Quereda, Juan M. Lopez-Sanchez, Fernando Vicente-Guijalba, Alexander W. Jacob, Marcus E. Engdahl// IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing – Vol. 13, 4070 – 4084, 2020.

Willians Ribeiro Mendes, Fábio Meneghetti U. Araújo, Ritaban Dutta, Derek M. Heeren. "Fuzzy control system for variable rate irrigation using remote sensing." Expert Systems With Applications 124 (2019): 13–24.

Yan Wang; M. Jamshidi. "Fuzzy logic applied in remote sensing image classification" IEEE In-ternational Conference on Systems, Man and Cybernetics (2004). doi: 10.1109/ICSMC.2004.1401402.

Katharina Heupel, Daniel Spengler, Sibylle Itzerott. "A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information" Journal of Photogram-metry, Remote Sensing and Geoinformation Science volume (2018): pp. 53–69.

Продовольча та сільськогосподарська організація ООН (ФАО), URL: http://www.fao.org.

Olena Kavats, Volodymyr Hnatushenko, Yuliya Kibukevych, Yurii Kavats. "Flood Monitoring Using Multi-Temporal Synthetic Aperture Radar Images" CSIT2019 (2019): 046, v3.

Olena Kavats,Dmitriy Khramov,Kateryna Sergieieva andVolodymyr Vasyliev Monitoring Har-vesting by Time Series of Sentinel-1 SAR Data, Remote Sens. 2019, 11(21), 2496; https://doi.org/10.3390/rs11212496

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Published

2024-10-03