Abstract
Wheat and rapeseed are significant crops in Czech agriculture and remote sensing has huge potential for their management, given Sentinel-1 can overcome issues of cloudiness and monitor vegetation development via radar backscatter. This study compares radar and optical data characterizing the development of wheat and rapeseed in an agricultural cooperative in the Czech Republic. Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) time-series of the main vegetation seasons between 2015 and 2018 are processed, analysed, and compared with each other. In 2018, the comparison of data with ground measurement by camera was also used. The temporal development of RVI is affected by noise, which is caused by the composition of imagery from different Relative orbits. The separation of imagery according to the Relative orbit used seemed to provide results more comparable to the phenological curve. Simple linear regression between NDVI and RVI illustrated that considering Relative orbit can slightly increase the Coefficient of determination. By selecting a suitable Relative orbit, the coefficient of determination between NDVI and RVI increased from 0.281 to 0.387 in the case of wheat and from 0.233 to 0.316 in the case of rape monitoring. The RVI for rapeseed and the height of canopy correlation was 0.392. The results for RVI presented in this article demonstrated that monitoring wheat and rapeseed development by Sentinel-1 has potential, however more research needs to be conducted in the areas of spatial and temporal noise removal.
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Acknowledgements
We would like to thank the management of the ZD Vendolí agriculture cooperative for providing agronomic data and Ass. Prof. Jan Kropáček for his valuable advices during data processing. This research was supported by the Internal Grant Agency of the Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague (Grant Number 20205012 and 20213108) and by the Internal Grant Agency of the Faculty of Engineering, Czech University of Life Sciences Prague, grant number 31160/1312/313117).
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Tůma, L., Kumhálová, J., Kumhála, F. et al. The noise-reduction potential of Radar Vegetation Index for crop management in the Czech Republic. Precision Agric 23, 450–469 (2022). https://doi.org/10.1007/s11119-021-09844-5
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DOI: https://doi.org/10.1007/s11119-021-09844-5