Monitoring Vegetative Stages of Spring Wheat (Triticum Aestivum L) with Sentinel-1 and Sentiel-2 Imagery in Bornuur soum, Tuv province, Mongolia
DOI:
https://doi.org/10.5564/mgs.v30i61.3957Keywords:
NDVI analysis, crop phenology, biomass estimation, remote sensing agriculture, vegetation indices, spatiotemporal monitoringAbstract
Spring wheat (Triticum aestivum Linnaeus, 1753) is critical for global food security, sustaining over 20% of the world’s population. In Mongolia, it is the primary staple crop, though production is affected by climatic and market fluctuations. This study, conducted at the “Nart” Research Center of the Mongolian University of Life Sciences in Bornuur soum, Tuv province, examined growth dynamics of the Darkhan-144 wheat variety using Sentinel-1 and Sentinel-2 satellite data. The crop, sown between May 21–25, 2020, achieved uniform germination within 15–20 days. Key phenophases included germination to main pricking (10 days), heading (15 days), flowering to milky seed stage (15 days), and milky to hybrid tuber (10 days), totaling a growth cycle of 85-90 days. The Normalized Difference Vegetation Index rose from (~0.18) in early May to 0.80 by July and September. Normalized Difference Vegetation Index showed strong correlation with the Normalized Difference Water Index for wet biomass (R²=0.67) and dry biomass (R²=0.62). Sentinel-2 reflectance ranged from 0.05-0.40 in May and July, and 0.25-0.45 in June. Field spectrometer values increased from 0.35 in July to 0.60 nm in August, before declining to 0.30 nm in September. These findings reveal a strong correlation between vegetation water indices and wheat growth parameters, highlighting the potential of satellite-based spatiotemporal analysis to inform and enhance local policymaking in agricultural production and management. This study supports the integration of remote sensing into Mongolia’s crop monitoring strategies.
Downloads
265
References
Brooks, D., Agosta, S. 2024b. Surviving the Anthropocene: A Darwinian Guide. Global Perspectives, vol. 5(1), 115331. https://doi.org/10.1525/gp.2024.115331
Brooks, D.R., Agosta, S.J. 2024a. A Darwinian Survival Guide: Hope for the Twenty-First Century. Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/15069.001.0001
Brooks, D.R., Hoberg, E.P., Boeger, W.A. 2019. The Stockholm paradigm: Climate change and emerging disease. Chicago, USA: University of Chicago Press, 400 p.
Gansukh, B., Batsaikhan, B., Dorjsuren, A., Jamsran, C., Batsaikhan, N. 2020. Monitoring wheat crop growth parameters using time series Sentinel-1 and Sentinel-2 data for agricultural application in Mongolia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLIII-B3-2020, 989-994. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-989-2020
Gao, B.C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, vol. 58(3), p. 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
Ibrahim, G.R.F., Rasul, A., Abdullah, H. 2023. Improving crop classification accuracy with integrated Sentinel-1 and Sentinel-2 data: A case study of barley and wheat. Journal of Geovisualization and Spatial Analysis, vol. 7, 22. https://doi.org/10.1007/s41651-023-00152-2
IPCC. 2021. Climate Change 2021: The Physical Science Basis. IPCC Sixth Assessment Report, The Physical Science Basis. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/
Islam, A.T., Islam, A.K.M.S., Islam, G.M.T., Bala, S.K., Salehin, M., Choudhury, A.K., Mahboob, M.G., Dey, N.C., Hossain, A. 2024. Monitoring wheat area using Sentinel-2 imagery and in-situ spectroradiometer data in heterogeneous field conditions. Discover Agriculture, vol. 2, 52. https://doi.org/10.1007/s44279-024-00069-4
Kornhuber, K., Osprey, S., Coumou, D., Petri, S., Petoukhov, V., Rahmstorf, S., Gray, L. 2019. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environmental Research Letters, vol. 14(5), 054002. https://doi.org/10.1088/1748-9326/ab13bf
Yuping, M., Shili, W., Li, Z, Yingyu, H., Liwei, Z., Yanbo, H., Futang, W. 2008. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 10(4), p. 426-437. https://doi.org/10.1016/j.jag.2007.09.002
Nduku, L., Munghemezulu, C., Mashaba-Munghemezulu, Z., Ratshiedana, P.E., Sibanda, S., Chirima, J.G. 2024. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning. AgriEngineering, 6(2), p. 1093-1116. https://doi.org/10.3390/agriengineering6020063
Segarra, J., Araus, J.L., Kefauver, S.C. 2022. Farming and Earth observation: Sentinel-2 data to estimate within-field wheat grain yield. International Journal of Applied Earth Observation and Geoinformation, vol. 107, 102697. https://doi.org/10.1016/j.jag.2022.102697
Shao, Y., Lunetta, R.S., Wheeler, B., Iiames, J S., Campbell, J. B. 2016. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multitemporal data. Remote Sensing of Environment, vol. 174, p. 258-265. https://doi.org/10.1016/j.rse.2015.12.023
Shewry, P.R. 2009. Wheat. Journal of Experimental Botany, vol. 60(6), p. 1537-1553. https://doi.org/10.1093/jxb/erp058
Shojaeezadeh, S.A., Elnashar, A., Weber, T. K. D. 2025. A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning. Science of Remote Sensing, vol. 11, 100227. https://doi.org/10.1016/j.srs.2025.100227
Toth, C., Jóźków, G. 2016. Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 115, p. 22-36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
Trivellone, V., Hoberg, E.P., Boeger, W.A., Brooks, D.R. 2022. Food security and emerging infectious disease: Risk assessment and risk management. Royal Society Open Science, 211687. https://doi.org/10.1098/rsos.211687
Tuvdendorj, B., Wu, B., Zeng, H., Batdelger, G., Nanzad, L. 2019. Determination of appropriate remote sensing indices for spring wheat yield estimation in Mongolia. Remote Sensing, vol. 11(21), 2568. https://doi.org/10.3390/rs11212568
Tuvshinbayar, D., Erdenetuya, B., Erkhembayar, E., Batbileg, B., Sarangerel, J. 2018. Some results of crop stress monitoring by remote sensing in Northern Mongolia. Mongolian Journal of Agricultural Sciences, vol. 21(02), p. 59-63. https://doi.org/10.5564/mjas.v21i02.906
Zhao, Y., Potgieter, A.B., Zhang, M., Wu, B., Hammer, G.L. 2020. Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sensing, vol. 12(6), 1024. https://doi.org/10.3390/rs12061024
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sanjaatjamts Lkhagvadulam, Gansukh Badamgarav, Batsaikhan Bayartungalag, Dursahinhan Tsogtsaikhan Altangerel, Bold Odgerel

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on any research article in the Mongolian Geoscientist is retained by the author(s).
The authors grant the Mongolian Geoscientist a license to publish the article and identify itself as the original publisher.

Articles in the Mongolian Geoscientist are Open Access articles published under a Creative Commons Attribution 4.0 International License CC BY.
This license permits use, distribution and reproduction in any medium, provided the original work is properly cited.