TY  - CONF
AU  - Blagojević, Dragana
AU  - Stojković, Stefanija
AU  - Brdar, Sanja
AU  - Crnojević, Vladimir
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3182
AB  - This research aimed to inspect the correlation coefficients, during the crop growth stages, between vegetation indices (VIs) derived from Sentinel-2 imagery and grain winter wheat yield derived from yield monitoring and select the most promising indices for monitoring crop growth and yield estimation. METHOD / DESIGN: The satellite images in 10m resolution were selected based on crop growth stages, from the end of tillering phase (beginning of March 2019) until the full ripening (end of June 2019). For the analysis, the BBCH-scale for cereals was used. Yield observations were performed at harvest on five fields in one season and twelve VIs were calculated across 10 growth stages. To designate their correlation and dependence, a statistical comparison of the VIs and yield was made. The Pearson’s and Spearman’s correlation coefficients were calculated, and their statistical significance was tested using p-value (at p=0.01, p=0.05). RESULTS: According to the crop growth stages, the highest correlation coefficients were detected from the early boot stage (BBCH 41) until the middle of development of the fruiting stage (BBCH 73 – early milk). In that period the correlation coefficients varied from 0.39 to 0.84 depending on the field. Based on the location, the highest correlation coefficient values for all 12 indices were recorded for the parcel named C-6 (April 15), and the lowest values for the parcel named C-10 (June 29). Most of the indices showed statistically significant dependence (at the p<0.01 and p<0.05 significant levels) on the yield in the first five growth stages except the chlorophyll vegetation index (CVI) for the parcel named C-11 (p=0.21, p=0.39). CONCLUSIONS: To conclude, the last growth stage named ripening showed the lowest values both for correlation coefficient and statistical significance which means that VIs also had low values because the reflectance is weak in this growth stage and wheat is about to be harvested. In the first five stages, VIs showed significantly high spectral reflectance values since in this period the leaf is full of chlorophyll pigments. Analyzing the correlation coefficient in different stages of wheat growth, we look at the current state of crops and have the opportunity to take appropriate measures in time to increase yields or save inputs at specific locations.
PB  - BioSense Institute, Novi Sad
C3  - International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference
T1  - Within-field correlation between satellite-derived vegetation indices and grain yield of wheat
SP  - 218
DO  - 10.13140/RG.2.2.22001.20324
ER  - 

@conference{
author = "Blagojević, Dragana and Stojković, Stefanija and Brdar, Sanja and Crnojević, Vladimir",
year = "2021",
abstract = "This research aimed to inspect the correlation coefficients, during the crop growth stages, between vegetation indices (VIs) derived from Sentinel-2 imagery and grain winter wheat yield derived from yield monitoring and select the most promising indices for monitoring crop growth and yield estimation. METHOD / DESIGN: The satellite images in 10m resolution were selected based on crop growth stages, from the end of tillering phase (beginning of March 2019) until the full ripening (end of June 2019). For the analysis, the BBCH-scale for cereals was used. Yield observations were performed at harvest on five fields in one season and twelve VIs were calculated across 10 growth stages. To designate their correlation and dependence, a statistical comparison of the VIs and yield was made. The Pearson’s and Spearman’s correlation coefficients were calculated, and their statistical significance was tested using p-value (at p=0.01, p=0.05). RESULTS: According to the crop growth stages, the highest correlation coefficients were detected from the early boot stage (BBCH 41) until the middle of development of the fruiting stage (BBCH 73 – early milk). In that period the correlation coefficients varied from 0.39 to 0.84 depending on the field. Based on the location, the highest correlation coefficient values for all 12 indices were recorded for the parcel named C-6 (April 15), and the lowest values for the parcel named C-10 (June 29). Most of the indices showed statistically significant dependence (at the p<0.01 and p<0.05 significant levels) on the yield in the first five growth stages except the chlorophyll vegetation index (CVI) for the parcel named C-11 (p=0.21, p=0.39). CONCLUSIONS: To conclude, the last growth stage named ripening showed the lowest values both for correlation coefficient and statistical significance which means that VIs also had low values because the reflectance is weak in this growth stage and wheat is about to be harvested. In the first five stages, VIs showed significantly high spectral reflectance values since in this period the leaf is full of chlorophyll pigments. Analyzing the correlation coefficient in different stages of wheat growth, we look at the current state of crops and have the opportunity to take appropriate measures in time to increase yields or save inputs at specific locations.",
publisher = "BioSense Institute, Novi Sad",
journal = "International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference",
title = "Within-field correlation between satellite-derived vegetation indices and grain yield of wheat",
pages = "218",
doi = "10.13140/RG.2.2.22001.20324"
}

Blagojević, D., Stojković, S., Brdar, S.,& Crnojević, V.. (2021). Within-field correlation between satellite-derived vegetation indices and grain yield of wheat. in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference
BioSense Institute, Novi Sad., 218.
https://doi.org/10.13140/RG.2.2.22001.20324

Blagojević D, Stojković S, Brdar S, Crnojević V. Within-field correlation between satellite-derived vegetation indices and grain yield of wheat. in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference. 2021;:218.
doi:10.13140/RG.2.2.22001.20324 .

Blagojević, Dragana, Stojković, Stefanija, Brdar, Sanja, Crnojević, Vladimir, "Within-field correlation between satellite-derived vegetation indices and grain yield of wheat" in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference (2021):218,
https://doi.org/10.13140/RG.2.2.22001.20324 . .

TY  - CONF
AU  - Radulović, Mirjana
AU  - Stojković, Stefanija
AU  - Pejak, Branislav
AU  - Lugonja, Predrag
AU  - Brdar, Sanja
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3167
AB  - In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management.
With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields.
Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy.
T1  - Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data
DO  - 10.5281/zenodo.4943800
ER  - 

@conference{
author = "Radulović, Mirjana and Stojković, Stefanija and Pejak, Branislav and Lugonja, Predrag and Brdar, Sanja",
year = "2021",
abstract = "In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management.
With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields.
Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy.",
title = "Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data",
doi = "10.5281/zenodo.4943800"
}

Radulović, M., Stojković, S., Pejak, B., Lugonja, P.,& Brdar, S.. (2021). Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data. .
https://doi.org/10.5281/zenodo.4943800

Radulović M, Stojković S, Pejak B, Lugonja P, Brdar S. Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data. 2021;.
doi:10.5281/zenodo.4943800 .

Radulović, Mirjana, Stojković, Stefanija, Pejak, Branislav, Lugonja, Predrag, Brdar, Sanja, "Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data" (2021),
https://doi.org/10.5281/zenodo.4943800 . .