1. PA
  2. All Volumes and Issues
  3. Volume 1, Number 1, 2025
  4. Assessment of Crop Condition Using Vegetation Indices Ndvi and Evi Based on Sentinel-2 Satellite Data – Two Case Studies

Assessment of Crop Condition Using Vegetation Indices Ndvi and Evi Based on Sentinel-2 Satellite Data – Two Case Studies

PA.
2025;
Volume 1, Number 1
: pp. 28 - 37
Authors:
  1. Borys Chetverikov
  2. Khrystyna Burshtynska
  3. Lyubov Babiy
  4. Nazar Hrytskiv
  5. Alla Hunina
  6. A. Kozak
1
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
2
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
3
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
4
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
5
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
6
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University

Objective. The study aims to assess the condition of agricultural crops using Sentinel-2 satellite imagery for two groups of agricultural plots located in the Lviv and Ternopil regions. The primary goal is to determine the spatio- temporal dynamics of vegetation indices NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which enables the identification of stressed vegetation zones and the general health level of crops throughout the growing season. Methods. The research employs remote sensing (RS) methods, photogrammetry, and geoinformation analysis. The input data–multispectral images from the Sentinel-2 satellite–were processed using the professional GIS environment ArcGIS. During the preprocessing stage, cloud masking, quality filtering, image normalization, and the calculation of vegetation indices were performed. The applied algorithms enabled accurate zoning of the territory based on vegetation development levels. Results. As a result of processing Sentinel-2 satellite images for the year 2024 on a monthly basis, a series of maps was generated, illustrating the spatial distribution of NDVI and EVI. The dynamic analysis revealed significant differences in vegetation conditions between the two regions, as well as within individual fields. Localized zones with reduced index values were identified, which may indicate the presence of stress factors such as drought, excessive moisture, pests, or errors in agrotechnical practices. Comparing index analysis results with field data validated the accuracy of satellite observations. Practical significance. The study results have direct practical application in the field of precision agriculture. Specifically, the developed NDVI and EVI distribution maps can be used by agronomists, farmers, and agricultural companies for real-time crop monitoring, early detection of problem areas, optimization of agrotechnical measures (e.g., fertilization, irrigation, or pest control), as well as for crop rotation planning and evaluating the effectiveness of applied technologies. The use of satellite imagery significantly reduces the need for labor-intensive field surveys, providing more accurate, objective, and regular data on vegetation status across large areas. This approach enhances agricultural efficiency, conserves resources, and minimizes the negative environmental impact.

precision agriculture
NDVI index
EVI index
Sentinel-2 satellite images
crop monitoring
remote sensing
  1. Dong, T., Liu, J., Shang, J., & Zhao, B. (2020). Mapping winter wheat with multi-temporal SAR and optical images: A case study in the North China Plain. Remote Sensing, 12(3), 456.
  2. Forkuor, G., Conrad, C., Thiel, M., Zoungrana, B., & Tondoh, J. E. (2014). Integration of optical and Synthetic Aperture Radar imagery for improving crop mapping in Northwestern Benin, West Africa. Remote Sensing, 6(7), 6472-6499.
  3. Gao, F., Anderson, M. C., Zhang, X., Yang, Z., Alfieri, J. G., Kustas, W. P., ... & Dulaney, W. (2020). Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 255, 112331.
  4. Hammond, J., & Dube, T. (2024). Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa. Remote Sensing Applications: Society and Environment, 35, 101230. DOI: https://doi.org/10.1016/j.rsase.2024.101230.
  5. Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., & Yang, G. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141-152.
  6. Jindain, P., & Thanawong, P. (2020). Comparison of NDVI and multispectral imageries of Sentinel-2 for detection of abandoned paddy field: A case study of eastern Phayao province. The Journal of Spatial Innovation Development, 10(2), 1–12. Retrieved from https://ph01.tci-thaijo.org/index.php/jsid/article/view/249556.
  7. Kalogeras, A., Bormpoudakis, D., Tsardanidis, I., Loka, D. A., & Kontoes, C. (2025). Monitoring digestate application on agricultural crops using Sentinel-2 satellite imagery. arXiv preprint arXiv:2504.19996. Retrieved from https://arxiv.org/abs/2504.19996.
  8. Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235-248.
  9. Lozano-Tello, A., Siesto, G., Fernández-Sellers, M., & Caballero-Mancera, A. (2023). Evaluation of the use of the 12 bands vs. NDVI from Sentinel-2 images for crop identification. Sensors, 23(16), 7132. DOI: https://doi.org/10.3390/s23167132.
  10. Liu, J., Pattey, E., & Miller, J. R. (2010). Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agricultural and Forest Meteorology, 150(11), 1485-1490.
  11. Meroni, M., Rembold, F., Verstraete, M. M., & Urbano, F. (2020). A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa. International Journal of Remote Sensing, 41(7),   2670-2689.
  12. Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., ... & Blackburn, G. A. (2015). Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation,  34,  188-197.
  13. Rashid, M. H., Rahman, M. M., & Islam, M. T. (2024). Monitoring wheat area using Sentinel-2 imagery and in-situ spectroradiometer data in heterogeneous field conditions. Discover Agriculture, 2(1), 69. DOI: https://doi.org/10.1007/s44279-024-00069-4.
  14. Sakamoto, T., Gitelson, A. A., Wardlow, B. D., Wardlow, B. D., & Egbert, S. L. (2010). A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas. International Journal of Remote Sensing, 31(3), 805–830. DOI: https://doi.org/10.1080/01431160902897858.
  15. Verma, S. B., Suyker, A. E., & Arkebauer, T. J. (2007). An alternative method using remote sensing technology to assess crop phenology in maize and soybean. Agricultural and Forest Meteorology, 148(3), 396-406.
  16. Zhang, X., Friedl, M. A., Schaaf, C. B., & Strahler, A. H. (2004). Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10(7), 1133-1145.