Assessing Drought Patterns Using Landsat-Derived Vegetation Health Index During Spring (2013–2024)

(Almustafa Abd Elkader Ayek , Suzan Fathe Karmoka , Abdullah Taher Hasan and Mohannad Ali Loho)

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Abstract

This study aims to assess drought patterns in the Harem region of northwestern Syria during the spring seasons from 2013 to 2024, using data from Landsat 8 and 9. Drought severity was evaluated and categorized into five classes using the Vegetation Health Index, which combines the Normalized Difference Vegetation Index and the Temperature Condition Index. Monthly precipitation data were included as a supporting component to evaluate climatic conditions, and the Google Earth Engine platform was used for processing and spatiotemporal analysis. The findings revealed fluctuations in drought severity, with the 2017–2019 period being the most affected, particularly in 2019, when land surface temperature (LST) reached its highest levels and vegetation health markedly declined. The 2020–2024 period, by contrast, showed gradual improvement in vegetation health and a reduction in the extent of severe drought. The study also indicated that the relationship between precipitation and drought severity is nonlinear, with LST playing a key role in determining drought intensity. To improve the management of water and agricultural resources and reduce the effects of drought on local communities and ecosystems, this study highlights the importance of adopting advanced monitoring and forecasting strategies that incorporate artificial intelligence technology. 
KEYWORDS
Abiotic stress, climate change, dissertation, prediction, water resources, weather forecast

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References

Adisa, O.M., Masinde, M., Botai, J.O. and Botai, C.M. (2020). Bibliometric analysis of methods and tools for drought monitoring and prediction in Africa. Sustainability, 12(16), 6516. DOI: 10.3390/su12166516.
Agarwal, V., Singh, B.V.R., Marsh, S., Qin, Z., Sen, A. and Kulhari, K. (2025). Integrated remote sensing for enhanced drought assessment: A multi‐index approach in Rajasthan, India. Earth and Space Science, 12(2), e2024EA003639.  DOI: 10.1029/2024EA003639. 
Alqadhi, S., Mallick, J. and Hang, H.T. (2025). Assessing drought trends and vegetation health in arid regions using advanced remote sensing techniques: A case study in Saudi Arabia. Theoretical and Applied Climatology, 156(1), 1–29. DOI: 10.1007/s00704-024-05301-1. 
 Atik, O., Kadour, A., Mahmoud, I., Al Hasan, K., Al Nabhan, A., Jazieh, H., Nijhawan, A. and Pianosi, F. (2024). Irrigation water in Northwest Syria: Impact of the recent crisis and drought. Water, 16(21), 3101. DOI: 10.3390/w16213101. 
Ayek, A.A. and Zerouali, B. (2025). Monitoring temporal changes of the Qttinah Lake surface area using Landsat data and Google Earth Engine. DYSONA–Applied Science, 6(1), 126–33. DOI: 10.30493/das.2024.476854.
Belward, A.S. and Skøien, J.O. (2015). Who launched what, when and why: Trends in global land-cover observation capacity from civilian Earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing, 103(n/a), 115–28. DOI: 10.1016/j.isprsjprs.2014.03.009.
Bhaga, T.D., Dube, T., Shekede, M.D. and Shoko, C. (2020). Impacts of climate variability and drought on surface water resources in Sub-Saharan Africa using remote sensing: A review. Remote Sensing, 12(24), 4184. DOI: 10.3390/rs12244184.
Bhowmik, S. and Bhatt, B. (2024). Drought monitoring using MODIS-derived indices and Google Earth Engine platform for Vadodara District, Gujarat. Journal of the Indian Society of Remote Sensing, 52(9), 1885–900. DOI: 10.1007/s12524-024-01922-1.
Bhuiyan, C., Singh, R.P. and Kogan, F.N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289–302. DOI: 10.1016/j.jag.2006.03.002.
Food and Agriculture Organization (FAO). (2020). Evaluation of FAO’s contribution to the Syrian Arab Republic 2012–2018 (Country Programme Evaluation Series, 12/2020). Rome. Available at: https://openknowledge.fao.org/handle/20.500.14283/cb2475en (accessed on 17/04/2025)
Ghaleb, F., Mario, M. and Sandra, A.N. (2015). Regional Landsat-based drought monitoring from 1982 to 2014. Climate, 3(3), 563–77. DOI: 10.3390/cli3030563.
Goward, S.N., Masek, J.G., Loveland, T.R., Dwyer, J.L., Williams, D.L., Arvidson, T., Rocchio, L.E.P. and Irons, J.R. (2021). Semi-centennial of Landsat observations and Pending Landsat 9 launch. Photogrammetric Engineering and Remote Sensing, 87(8), 533–9.  DOI: 10.14358/PERS.87.8.533.
Hameed, M., Ahmadalipour, A. and Moradkhani, H. (2020). Drought and food security in the Middle East: An analytical framework. Agricultural and Forest Meteorology, 281(n/a), 107816.  DOI: 10.1016/j.agrformet.2019.107816.
Hang, Q., Guo, H., Meng, X., Wang, W., Cao, Y., Liu, R., De Maeyer, P. and Wang, Y. (2024). Optimizing the Vegetation Health Index for agricultural drought monitoring: Evaluation and application in the Yellow River Basin. Remote Sensing, 16(23), 4507.  DOI: 10.3390/rs16234507.
Hu, T., van Dijk, A.I.J.M., Renzullo, L.J., Xu, Z., He, J., Tian, S., Zhou, J. and Li, H. (2020). On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations. International Journal of Applied Earth Observation and Geoinformation, 91(n/a), 102153.  DOI: 10.1016/j.jag.2020.102153.
Irons, J.R. and Masek, J.G. (2006). Requirements for a Landsat data continuity mission. Photogrammetric Engineering and Remote Sensing, 72(10), 1102. 
Jaafar, H.H., Zurayk, R., King, C., Ahmad, F. and Al-Outa, R. (2017). Impact of the Syrian conflict on irrigated agriculture in the Orontes Basin. In: The water-energy-food nexus in the Middle East and North Africa, Routledge.  DOI: 10.1080/07900627.2015.1023892.
Javan, F.D., Samadzadegan, F., Toosi, A. and TousiKordkolaei, H. (2025). Spatial-temporal patterns of agricultural drought severity in the Lake Urmia Basin, Iran: A cloud-based integration of multi-temporal and multi-sensor remote sensing data. DYSONA–Applied Science, 6(2), 239–61.  DOI: 10.30493/DAS.2025.486806.
Karmoka, S.F. and Hanjagi, D.A. (2023). Detecting the vegetation health situation of Tartus forests (Syria) and the spatio-temporal distribution of agricultural drought. In: International Scientific Conference of Young Scientists and Specialists Dedicated to the 180th Anniversary of the Birth of K.A. Timiryazev: Collection of Articles, 2(n/a), 527–32. 05–07/06/2023 DOI: 10.5281/zenodo.13693241.
Karmoka, S.F., Al haj Ahmad, A. and Alkhaled, E.A. (2019). Identifying drought classes in Northwest Syria using MODIS satellite image spectral indices EVI, LAI, TVI’. Scientific Journal of King Faisal University: Basic and Applied Sciences, 20(1), 27–39.  
Kim, T.W. and Jehanzaib, M. (2020). Drought risk analysis, forecasting and assessment under climate change. Water, 12(7), 1862.‏  DOI: 10.3390/w12071862. 
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100.  DOI: 10.1016/0273-1177(95)00079-T. 
Li, T. and Zhong, S. (2024). Advances in optical and thermal remote sensing of vegetative drought and phenology. Remote Sensing, 16(22), 4209.  DOI: 10.3390/rs16224209. 
Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J. and Jenstrom, D.T. (2020). Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, 248(n/a), 111968.  DOI: 10.1016/j.rse.2020.111968.
Masitoh, F. and Rusydi, A.N. (2019). Vegetation Health Index (VHI) analysis during drought season in Brantas Watershed. In: IOP Conference Series: Earth and Environmental Science 389(1), 012033. IOP Publishing.  DOI: 10.1088/1755-1315/389/1/012033.
Mathbout, S., Boustras, G., Papazoglou, P., Vide, J.M. and Raai, F. (2025). Integrating climate indices and land use practices for comprehensive drought monitoring in Syria: Impacts and implications. Environmental and Sustainability Indicators, 26(n/a), 100631.  DOI: 10.1016/j.indic.2025.100631.
Nepstad, D.C., de Carvalho, C.R., Davidson, E.A., Jipp, P.H., Lefebvre, P.A., Negreiros, G.H. and Vieira, S. (1994). The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature, 372(6507), 666–9.   DOI: 10.1038/372666a0.
Rouse Jr, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982).  Available at:  https://ntrs.nasa.gov/citations/19750020419 (accessed on 01/05/2025).
Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C. and Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145(n/a), 154–72.  DOI: 10.1016/j.rse.2014.02.001. 
Sugg, M., Runkle, J., Leeper, R., Bagli, H., Golden, A., Handwerger, L.H. and Woolard, S. (2020). A scoping review of drought impacts on health and society in North America. Climatic Change, 162(n/a), 1177–95.  DOI: 10.1007/s10584-020-02848-6.
Sukkar, A., Abulibdeh, A., Essoussi, S. and Seker, D.Z. (2024). Investigating the impacts of climate variations and armed conflict on drought and vegetation cover in Northeast Syria (2000–2023). Journal of Arid Environments, 225(n/a), 105278.  DOI: 10.1016/j.jaridenv.2024.105278.
Taheri Qazvini, A. and Carrion, D. (2023). A spatiotemporal drought analysis application implemented in the Google Earth Engine and applied to Iran as a case study. Remote Sensing, 15(9), 2218.  DOI: 10.3390/rs15092218.
Tang, J., Zeng, J., Zhang, Q., Zhang, R., Leng, S., Zeng, Y. and Wang, Q. (2020). Self-adapting extraction of cropland phenological transitions of rotation agroecosystems using dynamically fused NDVI images. International Journal of Biometeorology, 64(n/a), 1273–83.  DOI: 10.1007/s00484-020-01904-1.
Trenberth, K.E., Dai, A., Van der Schrier, G., Jones, P.D., Barichivich, J., Briffa, K.R. and Sheffield, J. (2014). Global warming and changes in drought. Nature Climate Change, 4(1), 17–22.  DOI: 10.1038/nclimate2067.
U.S. Geological Survey. (2023). Landsat 8–9 Collection 2 Level 2 Science Product Guide (Version 5.0). U.S. Department of the Interior.  Available at: https://www.usgs.gov (accessed on 07/02/2025)
Wilhite, D.A. and Glantz, M.H. (1985). Understanding the drought phenomenon: The role of definitions. Water International, 10(3), 111–20.  DOI: 10.1080/02508068508686328. 
Wulder, M.A., Roy, D.P., Radeloff, V.C., Loveland, T.R., Anderson, M.C., Johnson, D.M. and Cook, B.D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280(n/a), 113195.  DOI: 10.1016/j.rse.2022.113195.
Zeng, J., Zhang, R., Qu, Y., Bento, V.A., Zhou, T., Lin, Y. and Wang, Q. (2022). Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather and Climate Extremes, 35(n/a), 100412.  DOI: 10.1016/j.wace.2022.100412.
Zhang, Z., Xu, W., Shi, Z. and Qin, Q. (2021). Establishment of a comprehensive drought monitoring index based on multisource remote sensing data and agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14(n/a), 2113–26.  DOI: 10.1109/JSTARS.2021.3052194.
Yu, X., Guo, X. and Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829–52.  DOI: 10.3390/rs6109829.