USING NORMALIZED DIFFERENCE VEGETATION INDEX TO MONITOR ANNUAL WHITE MELILOT GROWTH AND DEVELOPMENT ON THE EARLY STAGES
DOI:
https://doi.org/10.36074/grail-of-science.17.10.2025.046Keywords:
early growth, Melilotus albus, remote sensing, Sentinel, soil mulchingSummary
Remote sensing data is of great importance in modern agriculture, as it is a source of valuable information that can be collected without direct disturbance of agricultural ecosystems. Remote crop growth monitoring is one of the essential application cases for remote sensing. However, it is still unclear whether spatial data is of any use to capture the development dynamics in sparse vegetation cover during the initial growth stages and soil mulching. The study is being performed in 2025 in France under mulching conditions with annual white melilot to determine if remote sensing data on the normalized difference vegetation index (NDVI) could be used to follow the dynamics of the crop establishment. The data from the Sentinel-2 satellite with 10 m resolution, pre-processed on the OneSoil cloud platform, was used to derive the NDVI dynamics in three annual white melilot fields for the period from July 10th to September 16th. As a result, it was determined that in general, NDVI correctly represents in-field dynamics of the crop growth during the initial stages of its development notwithstanding the fact of possible disturbances from soil mulching. Therefore, remote sensing data could be used for spatial monitoring of crop growth even under uncommon cultivation conditions.
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Copyright (c) 2025 Pavlo Lykhovyd, Dmytro Maksymov
References
Allashov, B., Zulfikarov, M., & Toreev, F. (2020). Effective agrotechnology for cultivation of forage crops. IOP Conference Series: Earth and Environmental Science, 614, 012159. https://doi.org/10.1088/1755-1315/614/1/012159 DOI: https://doi.org/10.1088/1755-1315/614/1/012159
Benincasa, P., Antognelli, S., Brunetti, L., Fabbri, C., Natale, A., Sartoretti, V., Modeo, G., Guiducci, M., Tei, F., & Vizzari, M. (2017). Reliability of NDVI derived by high resolution satellite and UAV compared to in-field methods for the evaluation of early crop N status and grain yield in wheat. Experimental Agriculture, 54, 604-622. https://doi.org/10.1017/S0014479717000278 DOI: https://doi.org/10.1017/S0014479717000278
Birnbaum, D., Ely, J. W., Dawson, J. D., Lemke, J. H., & Rosenberg, J. (1997). An introduction to time-trend analysis. Infection Control & Hospital Epidemiology, 18(4), 267-274. https://doi.org/10.1086/647609 DOI: https://doi.org/10.1086/647609
Farias, G., Bremm, C., Bredemeier, C., De Lima Menezes, J., Alves, L., Tiecher, T., Martins, A., Fioravanço, G., Da Silva, G., & De Faccio Carvalho, P. (2023). Normalized Difference Vegetation Index (NDVI) for soybean biomass and nutrient uptake estimation in response to production systems and fertilization strategies. Frontiers in Sustainable Food Systems, 6, 959681. https://doi.org/10.3389/fsufs.2022.959681 DOI: https://doi.org/10.3389/fsufs.2022.959681
Gianquinto, G., Orsini, F., Pennisi, G., & Bona, S. (2019). Sources of variation in assessing canopy reflectance of processing tomato by means of multispectral radiometry. Sensors, 19(21), 4730. https://doi.org/10.3390/s19214730 DOI: https://doi.org/10.3390/s19214730
Karthikeyan, L., Chawla, I., & Mishra, A. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586, 124905. https://doi.org/10.1016/j.jhydrol.2020.124905 DOI: https://doi.org/10.1016/j.jhydrol.2020.124905
Lykhovyd, P. (2021). Forecasting oil crops yields on the regional scale using normalized difference vegetation index. Journal of Ecological Engineering, 22(3), 53-57. https://doi.org/10.12911/22998993/132436 DOI: https://doi.org/10.12911/22998993/132436
Lykhovyd, P. (2021). Study of climate impact on vegetation cover in Kherson Oblast (Ukraine) using normalized difference and enhanced vegetation indices. Journal of Ecological Engineering, 22(6), 126-135. https://doi.org/10.12911/22998993/137362 DOI: https://doi.org/10.12911/22998993/137362
Naser, M., Khosla, R., Longchamps, L., & Dahal, S. (2020). Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sensing, 12, 824. https://doi.org/10.3390/rs12050824 DOI: https://doi.org/10.3390/rs12050824
Rosle, R., Che’Ya, N., Roslin, N., Halip, R., & Ismail, M. (2019). Monitoring early stage of rice crops growth using normalized difference vegetation index generated from UAV. IOP Conference Series: Earth and Environmental Science, 355, 012066. https://doi.org/10.1088/1755-1315/355/1/012066 DOI: https://doi.org/10.1088/1755-1315/355/1/012066
Stagnari, F., Galieni, A., Speca, S., Cafiero, G., & Pisante, M. (2014). Effects of straw mulch on growth and yield of durum wheat during transition to Conservation Agriculture in Mediterranean environment. Field Crops Research, 167, 51-63. https://doi.org/10.1016/J.FCR.2014.07.008 DOI: https://doi.org/10.1016/j.fcr.2014.07.008
Vozhegova, R., Vlaschuk, A., Drobit, O., & Vlaschuk, O. (2021). The yield of white melilot seeds depending on the width between rows and doses of nitrogen fertilizer. Bulletin of Agricultural Science, 5(818), 16-22. https://doi.org/10.31073/agrovisnyk202105-02 DOI: https://doi.org/10.31073/agrovisnyk202105-02
Vozhehova, R., Maliarchuk, M., Biliaieva, I., Lykhovyd, P., Maliarchuk, A., & Tomnytskyi, A. (2020). Spring row crops productivity prediction using normalized difference vegetation index. Journal of Ecological Engineering, 21(6), 176-182. https://doi.org/10.12911/22998993/123473 DOI: https://doi.org/10.12911/22998993/123473
Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A., Qin, X., Yan, N., Chang, S., Zhao, Y., Dong, Q., Boken, V., Plotnikov, D., Guo, H., Wu, F., Zhao, H., Deronde, B., Tits, L., & Loupian, E. (2022). Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4), nwac290. https://doi.org/10.1093/nsr/nwac290 DOI: https://doi.org/10.1093/nsr/nwac290
Zamani-Noor, N., & Feistkorn, D. (2022). Monitoring growth status of winter oilseed rape by NDVI and NDYI derived from UAV-based Red–Green–Blue imagery. Agronomy, 12(9), 2212. https://doi.org/10.3390/agronomy12092212 DOI: https://doi.org/10.3390/agronomy12092212
Zhang, D., Qi, H., Guo, X., Sun, H., Min, J., Li, S., Hou, L., & Lv, L. (2025). Integration of UAV multispectral remote sensing and random forest for full-growth stage monitoring of wheat dynamics. Agriculture, 15(3), 353. https://doi.org/10.3390/agriculture15030353 DOI: https://doi.org/10.3390/agriculture15030353
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