TY  - JOUR
AU  - Varghese, Dani
AU  - Radulović, Mirjana
AU  - Stojković, Stefanija
AU  - Crnojević, Vladimir
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3168
AB  - This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.
PB  - MDPI
T1  - Reviewing the potential of Sentinel-2 in assessing the drought
VL  - 13: 3355
DO  - 10.3390/rs13173355
ER  - 

@article{
author = "Varghese, Dani and Radulović, Mirjana and Stojković, Stefanija and Crnojević, Vladimir",
year = "2021",
abstract = "This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.",
publisher = "MDPI",
title = "Reviewing the potential of Sentinel-2 in assessing the drought",
volume = "13: 3355",
doi = "10.3390/rs13173355"
}

Varghese, D., Radulović, M., Stojković, S.,& Crnojević, V.. (2021). Reviewing the potential of Sentinel-2 in assessing the drought. 
MDPI., 13: 3355.
https://doi.org/10.3390/rs13173355

Varghese D, Radulović M, Stojković S, Crnojević V. Reviewing the potential of Sentinel-2 in assessing the drought. 2021;13: 3355.
doi:10.3390/rs13173355 .

Varghese, Dani, Radulović, Mirjana, Stojković, Stefanija, Crnojević, Vladimir, "Reviewing the potential of Sentinel-2 in assessing the drought", 13: 3355 (2021),
https://doi.org/10.3390/rs13173355 . .

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 . .