RESEARCH PAPER
Monitoring of winter wheat growth in categories of frost damage and production potential using SAR and optical images
1
Department of Agricultural Machines, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague 6-Suchdol, Czech Republic
2
Department of Vehicles and Ground Transport, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague 6-Suchdol, Czech Republic
3
Department of Mathematics, Czech University of Life Sciences Prague,
Kamýcká 129, 165 21 Prague 6-Suchdol, Czech Republic
Final revision date: 2024-10-22
Acceptance date: 2024-11-08
Publication date: 2025-01-28
Corresponding author
Jitka Kumhálová
Department of Vehicles and Ground Transport, Czech University of Life Sciences Prague, Czech Republic
Department of Vehicles and Ground Transport, Czech University of Life Sciences Prague, Czech Republic
Int. Agrophys. 2025, 39(2): 99-111
HIGHLIGHTS
- Information from SAR images can be used to assess within field variability
- RVI, VH and VV polarization showed differences between observed categories
- Logistic regression model resulted better for higher spatial resolutions
- Modern statistical methods bring information from SAR closer to practical use
KEYWORDS
TOPICS
ABSTRACT
Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.
ACKNOWLEDGEMENTS
We would like to thank to the management of agriculture company Lupofyt for providing agronomic data.
FUNDING
This work was supported by the Ministry of Agriculture of the Czech Republic, research project NAZV QK 22010014; and Internal Grant of Faculty of Engineering CZU 2021:31150/1312/3113.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
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