MONITORING AGRICULTURAL DROUGHT IN REGIONS OF IRAQ USING REMOTE SENSING TECHNIQUE
DOI:
https://doi.org/10.36103/j9wkjc12Keywords:
vegetation health index, temperature condition index, normalized difference vegetation index, GIS, drought indicesAbstract
The phenomenon of drought is one of the most significant environmental and climatic extreme events; it is more complex in terms of measuring, monitoring, and identifying the possible effects and hazards associated. The remote sensing index, which included the Vegetation Health Index (VHI), was used to describe the geographic and temporal distribution of the springtime agricultural drought. VHI can be calculated based on the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Normalized Difference Vegetation Index (NDVI) derived from MODIS Terra satellite data. The outcomes showed that the droughts in the years 2001–2002, 2008–2009 were the most extreme in twenty-two years. The drought years 2001-2002 were followed by the drought-free years 2002/2003,2008/2009. The years 2017-2018 show this phenomenon distributed mostly in the south and southwest bearing. According to the findings, the southwest and western regions are more vulnerable to drought. The results of the drought index VHI indicate that the country is facing non-uniform cycles of drought for all types of droughts. Also, the results showed that there is a negative significance between rainfall and extreme, severe, and moderate drought, even if there was no substantial negative link between rainfall and dryness. It is recommended to adopt the VHI index to monitor the desiccation of vegetation in semi-arid areas with little access to surface meteorological information.
References
1. Abbas S., N., Nichol, F., Qamer, and J., Xu., 2014. Characterization of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China. Remote Sensing 6: 4998-5018. https://doi.org/10.3390/RS6064998.
2. Abdul-Jabbar, A. M., A. K Abdulkareem., 2021. Predicted the Cumulative Annual Rainfall in Iraq using SDSM Modal. Al-Mustansiriyah Journal of Science, 32(2). https://doi.org/10.23851/MJS.V32I2.977
3. Al-Gurairy, A., and A. Al-Zubaidi., 2023. Climate change and its impact on the expansion of the phenomenon of sand dunes and desertification of agricultural lands in Iraq for the period 1984-2022 (Governorates of Al-Qadisiyah, Al-Muthanna, and Dhi Qar). Israa Univ J Appl Sci.7(1), 75-96. https://doi.org/10.52865/YJPI8019
4. Ali, S. H., A. R. Qubaa,and A. B. M. Al-Khayat., 2024. Climate change and its potential impacts on Iraqi environment: Overview. In IOP Conference Series: Earth and Environmental Science .1300(1). IOP Publishing.
https://doi.org/10.1088/1755-1315/1300/1/012010
5. Alito, K. T., M. S. Kerebih, and D. A. Hailu., 2025. Characterization of Drought Detection With Remote Sensing Based Multiple Indices and SPEI in Northeastern Ethiopian Highland. Air, Soil and Water Research. 18(8). https://doi.org/10.1177/11786221251328833
6. Al-Timimi, Y. K., Al-Lami, A. M., Basheer, F. S., and Awad, A. Y. 2024. Impacts of climate change on thermal bioclimatic indices over Iraq. Iraqi Journal of Agricultural Sciences, 55(2): 744-756.
https://doi.org/10.36103/j93nst49
7. Basheer, F. S., 2022. Trend Analysis of Annual Surface Air Temperature for Some Stations over Iraq. Al-Mustansiriyah Journal of Science, 33(1), 77-82.
https://doi.org/10.23851/mjs.v33i1.1083.
8. Bayarjargal Y, A., Karnieli, M., Bayasgalan, S., Khudulmur, C., and Gandush, et al., 2006. A comparative study of NOAA-AVHRR derived drought indices using change vector analysis. Remote sensing of Environment 105: 9-22. https://doi.org/10.1016/J.RSE.2006.06.003
9. Burka, A., B. Biazin, and W. Bewket, 2024. Spatial drought occurrences and distribution using VCI, TCI, VHI, and Google Earth engine in bilate river watershed, rift Valley of Ethiopia. Geomatics, Natural Hazards and Risk.15(1). https://doi.org/10.1080/19475705.2024.2377672
10. Caccamo G, L. A., Chisholm, R. A., Bradstock, M. L., Puotinen, 2011. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote sensing of Environment 115: 2626-2639. https://doi.org/10.1016/J.RSE.2011.05.018.
11. Campbell J. B., 2006. Introduction in to Remote Sensing", (4thedn). Guilford Publications, New York, USA. https://doi.org/10.2307/3451502
12. Dos Santos Araujo, D. C., S. M. G. L. Montenegro, A. R. Neto, and S. F. da Silva., 2024. Evaluation of satellite-based soil moisture for agricultural drought monitoring in the Brazilian semiarid region. Remote Sensing Applications: Society and Environment 33, 101111. https://doi.org/10.1016/j.rsase.2023.101111
13. Du L, Q., Tian, T., Yu, Q., Meng, T., Juncos, 2013. A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation 23:245- 253. https://doi.org/10.1016/J.JAG.2012.09.010
14. Fadhil, A. M., 2013. Sand dunes monitoring using remote sensing and GIS techniques for some sites in Iraq. In Proceedings of the 3rInternational Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), Sanya, China, 27–28 January 2013.
15. Haktanir, K., 2004. Environmental Challenges in the Mediterranean 2000–2050; Springer: Berlin/Heidelberg, Germany.
16. Hang, Q., Guo, H., X. Meng, W. Wang, Y. Cao, R. Liu, and Y. Wang., 2024. Optimizing the vegetation health index for agricultural drought monitoring: evaluation and application in the yellow river basin. Remote Sensing.16(23): 4507. https://doi.org/10.3390/rs16234507
17. Heim R. R., 2002. A review of twentieth-century drought indices used in the United States. American Meteorological Society 83: 1149-1165.
18. IdRC. 2012. Capacity Assessment for Drought Risk Management in Iraq. Final Report; Submitted to: United Nations Development Programme (UNDP); Interdisciplinary Research Consultants (IdRC): Amman, Jordan.
19. IPCC, 2007. The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge United Press, Cambridge, United Kingdom, and New York, USA. no: 996
20. Kogan F. N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 11: 91-100. https://doi.org/10.1016/0273-1177(95)00079-T
21. Kogan F. N., 1997. Global drought watches from space. Bulletin of the American Meteorological Society 78: 621-636. https://doi.org/10.1175/1520-0477(1997)078<0621: GDWFS>2.0.CO;2
22. Li X, Q., Zhang, and X., Ye, 2013. Dry/Wet Conditions Monitoring Based on TRMM Rainfall Data and Its Reliability Validation over Poyang Lake Basin, China. Water 5: 1848-1864. https://doi.org/10.3390/W5041848
23. Liu, Q., S. Zhang, H. Zhang, Y. Bai, and J. Zhang., 2020. Monitoring drought using composite drought indices based on remote sensing. Science of the total environment. 711, 134585. https://doi.org/10.1016/j.scitotenv.2019.134585
24. Magno, R., T. De Filippis, E. Di Giuseppe, M. Pasqui, L. Rocchi, and B. Gozzini., 2018. Semi-automatic operational service for drought monitoring and forecasting in the Tuscany region. Geosciences. 8(2):49. https://doi.org/10.3390/geosciences8020049
25. Muter, S. A., Al-Jiboori, M. H., and Al-Timimi, Y. K., 2025. Assessment of spatial and temporal monthly rainfall trend over Iraq. Baghdad Science Journal, 22(3): 910-922.
https://doi.org/10.21123/bsj.2024.10367
26. Nedham, U. S., and A.S., Hassan, 2019. Comparison of some drought indices in Iraq. Al-Mustansiriyah Journal of Science, 30(4), 1-9. https://doi.org/10.23851/MJS.V30I4.674
27. Nichol, S. N., F. Qamer, and J. Xu, 2014. Characterisation of Drought Development through Remote Sensing: A Case Study in Central Yunnan, China. Remote Sensing 6: 4998-5018. https://doi.org/10.3390/RS6064998.
28. Nikdad, P., M.Mohammadi Ghaleni, M. Moghaddasi, and B. Pradhan., 2024. Enhancing a machine learning model for predicting agricultural drought through feature selection techniques. Applied Water Science. 14(6), 125. https://doi.org/10.1007/s13201-024-02193-4
29. Othman, A., Al-Saady, Y., Shihab, A., and A. abd Al-Maamar, 2020. Environmental Remote Sensing and GIS in Iraq. Environmental Remote Sensing and GIS in Iraq, Springer, Cham, 279-300.
30. Parida B. R, B. Oinam, 2008. Drought monitoring in India and the Philippines with satellite remote sensing measurements. EARSeL e Proceedings 7: 81-91. https://doi.org/10.23953/CLOUD.IJARSG.299
31. Rhee J, J. Im, G. J. Carbone, 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensor data. Remote Sensing of Environment 114: 2875 – 2887. https://doi.org/10.1016/J.RSE.2010.07.005
32. Rouse J. W, R. H. Haas, J. A. Schell, D. W. Deering., 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings. Third Earth Resources Technology Satellite-1 Symposium, Greenbelt: NASA, 309- 317.
33. Saeed, F. H., M. S. Al-Khafaji, and F. Al-Faraj., 2022. Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate. Journal of Water and Climate Change. 13(3), 1225-1240. https://doi.org/10.2166/wcc.2022.418
34. Sandholt I, K. Rasmussen, and J. Andersen., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of the Environment 79: 213-224.
35.Serban, C., and C. Maftei., 2025. Remote Sensing Evaluation of Drought Effects on Crop Yields Across Dobrogea, Romania, Using Vegetation Health Index (VHI). Agriculture.15(7), 668. https://doi.org/10.3390/agriculture15070668
36. Tao, H., B. M. Hashim, S. Heddam, L. Goliatt, M. L. Tan, Z. Sa’adi, and Z. M. Yaseen., 2023. Megacities’ environmental assessment for Iraq region using satellite image and geo-spatial tools. Environmental Science and Pollution Research.30(11): 30984-31034.
https://doi.org/10.1007/s11356-022-24153-8
37. Tsiros E, C. Domenikiotis, M. Spiliotopoulos, and N. R. Dalezios., 2004, Use of NOAA/A VHRR-based Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) for drought monitoring in Thessaly, Greece.
38. Wu, W., A. S., Muhaimeed, W. M., Al-Shafie, and A. M. F. Al-Quraishi, 2019.Using L-band radar data for soil salinity mapping A case study in Central Iraq. Environ. Res.
39. Yang B, Q. Wang, C. Wang, H. Wan, and Y. Yang, 2010. Drought monitoring in North China using HJ-1 satellite remote sensing data. Proceedings of the SPIE 7841.
40. Zarei R, M., Sarajian, and S., Bazgeer, 2013. Monitoring Meteorological Drought in Iran Using Remote Sensing and Drought Indices. Desert 18: 89-97. https://doi.org/10.22059/JDESERT.2013.36279
41. Zhang A, G. Jia, 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment 134: 12-23. https://doi.org/10.1016/J.RSE.2013.02.023
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