Research article
● Open access
Monitoring and Assessment of Agricultural Drought Using Satellite Data: A Comprehensive Case Study in the Afaj District, Al-Qadisiyah Governorate, Southern Iraq
Abstract
Drought is one of the most severe environmental hazards affecting water resources, agriculture, and ecosystems, particularly in arid and semi-arid regions. Iraq is highly vulnerable to climate change and reduced water inflows from the Tigris and Euphrates rivers, resulting in increasing drought severity and land degradation. This study analyzes the spatiotemporal distribution of drought in Afaj District, Al-Qadisiyah Governorate, southern Iraq, using Sentinel-2 satellite imagery and remote sensing indices including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) for the years 2019 and 2025.
Satellite data with 10 m spatial resolution were processed to assess vegetation health, surface moisture, and drought intensity. Statistical analysis revealed a strong positive correlation between NDVI and NDWI (r ≈ 0.69) and strong negative correlations between NDDI and both NDVI and NDWI, confirming the reliability of these indices for drought monitoring. Land cover analysis showed significant environmental changes, with bare land increasing by approximately 28% between 2019 and 2025, while vegetation and water-covered areas declined.
The results indicate a substantial intensification of drought conditions, with areas experiencing moderate to high drought severity increasing from 48% in 2019 to 75% in 2025. These changes highlight the growing risks of desertification, agricultural decline, and ecosystem degradation in southern Iraq. The study demonstrates that integrated remote sensing indices provide an effective approach for monitoring drought dynamics and supporting sustainable water and land management strategies.
Keywords
Drought Monitoring, Remote Sensing, Sentinel-2, NDVI, NDWI, NDDI, Land Cover Change, Desertification, Climate Change, Water Scarcity, Iraq.
References
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Jiang, J., & Zhou, T. (2023). Agricultural drought over water-scarce Central Asia aggravated by internal climate variability. Nature Geoscience, 16, 154-161. https://doi.org/10.1038/s41561-022-01111-0
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Lazo, P. X., Mosquera, G. M., McDonnell, J. J., & Crespo, P. (2019). The role of vegetation, soils, and precipitation on water storage and hydrological services in Andean Páramo catchments. Journal of Hydrology, 572, 805-819. https://www.sciencedirect.com/science/article/pii/S002216941930246X
Le Hung, T., & Tuyen, V. D. (2019). Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam). Russian Journal of Earth Sciences, 19(2), 1-9. https://cyberleninka.ru/article/n/application-of-remote-sensing-technique-for-drought-assessment-based-on-normalized-difference-drought-index-a-case-study-of-bac-binh
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Muhaimeed, A. S., Nassar, M., & Katalan, B. (2025b). The impact of climate change on moisture balance and land degradation in the region of Southern Iraq. International Journal of Agriculture Innovations and Research, 13(6), 118-136. https://www.ijair.org/issues.php?id=13-6-2025#10
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Paniagua, M. T., Villalba, J., & Pasten, M. (2020). Spatial-temporal distribution of drought in the western region of Paraguay (2005-2017). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3W12-2020), 327-330. https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-327-2020
Renza, D., Martinez, E., Arquero, A., & Sanchez, J. (2010). Drought estimation maps by means of multidate Landsat fused images. In Proceedings of the 30th EARSeL Symposium (pp. 775-782). https://www.academia.edu/download/105493185/EARSeL-Symposium-2010_17-03.pdf
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, 1, 309-317.
Salas-Martinez, F., Valdes-Rodriguez, O. A., Palacios-Wassenaar, O. M., Marquez-Grajales, A., & Rodriguez-Hernandez, L. D. (2023). Methodological estimation to quantify drought intensity based on the NDDI index with Landsat 8 multispectral images in the central zone of the Gulf of Mexico. Frontiers in Earth Science, 11, 1027483. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1027483/full
Sharifi, A. (2020). Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran. Journal of the Indian Society of Remote Sensing, 48(9), 1289-1296. https://doi.org/10.1007/s12524-020-01155-y
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Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://www.sciencedirect.com/science/article/pii/0034425779900130
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Alhumaima, A. S., & Abdullaev, S. M. (2020). Tigris Basin landscapes: Sensitivity of vegetation index NDVI to climate variability derived from observational and reanalysis data. Earth Interactions, 24(7), 1-18. https://doi.org/10.1175/EI-D-20-0002.1
Artikanur, S. D., Widiatmaka, Y., Setiawan, Y., & Marimin. (2022). Normalized difference drought index (NDDI) computation for mapping drought severity in Bojonegoro Regency, East Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 1109(1), 012027. https://doi.org/10.1088/1755-1315/1109/1/012027
Belal, A. A., El-Ramady, H. R., Mohamed, E. S., & Saleh, A. M. (2014). Drought risk assessment using remote sensing and GIS techniques. Arabian Journal of Geosciences, 7(1), 35-53. https://doi.org/10.1007/s12517-012-0707-2
Chandrasekara, S. S. K., Kwon, H.-H., Vithanage, M., Obeysekera, J., & Kim, T.-W. (2021). Drought in South Asia: A review of drought assessment and prediction in South Asian countries. Atmosphere, 12(3), 369. https://doi.org/10.3390/atmos12030369
Charat, M., Wattanakij, N., Kamchai, T., Mongkolsawat, K., & Chuyakhai, D. (2009). Exploration of spatio-temporal drought patterns using satellite-derived indices for crop management in Northeastern Thailand. Asian Association on Remote Sensing, Beijing, China.
Cheng-lin, L., & Jian-jun, W. (2008). Crop drought monitoring using MODIS NDDI over mid-territory of China. In *IGARSS 2008 - IEEE International Geoscience and Remote Sensing Symposium* (Vol. 3, pp. III-883). IEEE. https://ieeexplore.ieee.org/abstract/document/4779491/
Dobri, R.-V., Sfică, L., Amihăeşei, V.-A., Apostol, L., & Timpu, S. (2021). Drought extent and severity on arable lands in Romania derived from normalized difference drought index (2001-2020). Remote Sensing, 13(8), 1478. https://doi.org/10.3390/rs13081478
Du, T. L. T., Bui, D. D., Nguyen, M. D., & Lee, H. (2018). Satellite-based multi-indices for evaluation of agricultural droughts in a highly dynamic tropical catchment, Central Vietnam. Water, 10(5), 659. https://www.mdpi.com/2073-4441/10/5/659
Elusma, M., Tung, C. P., & Lee, C. C. (2022). Agricultural drought risk assessment in the Caribbean region: The case of Haiti. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2022.103414
Goward, S. N., Markham, B. L., Dye, D. G., Dulaney, W. P., & Yang, J. (1991). Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sensing of Environment, 35, 257-277. https://doi.org/10.1016/0034-4257(91)90017-Z
Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters, 34(6). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2006GL029127
Gu, Y., Hunt, E., Wardlow, B., Basara, J. B., Brown, J. F., & Verdin, J. P. (2008). Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35, L22401. https://doi.org/10.1029/2008GL035772
Guo, Y., Huang, S., Huang, Q., Leng, G., Fang, W., Wang, L., & Wang, H. (2020). Propagation thresholds of meteorological drought for triggering hydrological drought at various levels. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.136502
Halin, H. (2018). Pengaruh kualitas produk terhadap kepuasan pelanggan semen baturaja di Palembang pada PT Semen Baturaja (Persero) Tbk. Jurnal Ecoment Global, 3(2), 167-182. https://doi.org/10.35908/jeg.v3i2.477
Hereher, M., Alghmdi, A., Mesdedi, K., & El Kenawy, A. (2022). Remote sensing of vegetation prolonged drought at the salt plays of Hail - Saudi Arabia. The Egyptian Journal of Remote Sensing and Space Science, 25, 135-145. https://doi.org/10.1016/j.ejrs.2022.01.006
Hollinger, S. E., Isard, S. A., & Welford, M. R. (1993). A new soil moisture drought index for predicting crop yields. In Preprints, Eighth Conference on Applied Climatology (pp. 187-190). American Meteorological Society, Anaheim, CA, USA.
Huang, B., Yuan, Z., Zheng, M., Liao, Y., Nguyen, K. L., Nguyen, T. H., Sombatpanit, S., & Li, D. (2022). Soil and water conservation techniques in tropical and subtropical Asia: A review. Sustainability, 14(9), 5035. https://doi.org/10.3390/su14095035
Jameel, M., Hameed, S., Shemal, K., Al-Ansari, N., & Abed, S. A. (2023). Spatial and temporal assessment of drought in the northern prone of Iraq using standardized precipitation index. Engineering, 15(10), 691-708. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1808855
Jiang, J., & Zhou, T. (2023). Agricultural drought over water-scarce Central Asia aggravated by internal climate variability. Nature Geoscience, 16, 154-161. https://doi.org/10.1038/s41561-022-01111-0
Khalili, P., Konar, M., & Faramarzi, M. (2024). Modelling the impacts of future droughts and post-droughts on hydrology, crop yields, and their linkages through assessing virtual water trade in agricultural watersheds of high-latitude regions. Journal of Hydrology, 639, 131530. https://www.sciencedirect.com/science/article/pii/S0022169424009260
Khampeera, A., Yongchalermchai, C., & Techato, K. (2018). Drought monitoring using drought indices and GIS techniques in Kuan Kreng peat swamp, Southern Thailand. Walailak Journal of Science and Technology, 15(5), 357-370. https://doi.org/10.48048/wjst.2018.2723
Laosuwan, T., Sangpradid, S., Gomasathit, T., & Rotjanakusol, T. (2016). Application of remote sensing technology for drought monitoring in Mahasarakham Province, Thailand. International Journal of Geoinformatics, 12(3), 17-25. https://www.geoinfo.ait.ac.th/index.php/jig/article/view/100
Lazo, P. X., Mosquera, G. M., McDonnell, J. J., & Crespo, P. (2019). The role of vegetation, soils, and precipitation on water storage and hydrological services in Andean Páramo catchments. Journal of Hydrology, 572, 805-819. https://www.sciencedirect.com/science/article/pii/S002216941930246X
Le Hung, T., & Tuyen, V. D. (2019). Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam). Russian Journal of Earth Sciences, 19(2), 1-9. https://cyberleninka.ru/article/n/application-of-remote-sensing-technique-for-drought-assessment-based-on-normalized-difference-drought-index-a-case-study-of-bac-binh
Lee, S.-J., Cho, J., Hong, S., Ha, K.-J., Lee, H., & Lee, Y.-W. (2016). On the relationships between satellite-based drought index and gross primary production in the North Korean croplands, 2000-2012. Remote Sensing Letters, 7(8), 790-799. https://doi.org/10.1080/2150704X.2016.1187315
Lemenkova, P. (2015). Analysis of Landsat NDVI time series for detecting degradation of vegetation. In Geoecology and Sustainable Use of Mineral Resources: From Science to Practice: Proceedings of the 3rd International Conference of Young Scientists. https://doi.org/10.6084/m9.figshare.7211795
Lin, X., Niu, J., Berndtsson, R., Yu, X., Zhang, L., & Chen, X. (2020). NDVI dynamics and its response to climate change and reforestation in northern China. Remote Sensing, 12(24), 4138. https://doi.org/10.3390/rs12244138
Matas-Granados, L., Pizarro, M., Cayuela, L., Domingo, D., Gómez, D., & García, M. B. (2022). Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants. Biological Conservation, 265, 109428. https://www.sciencedirect.com/science/article/pii/S0006320721004808
McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (pp. 179-183). https://climate.colostate.edu/pdfs/McKee_Doesken_Kleist_1993.pdf
Muhaimeed, A. S., Nassar, M. S., & Katalan, B. A. (2025a). The threat of climate change to vegetation health and land degradation in Iraq's diverse climatic environment. American Journal of Agriculture and Forestry, 13(4), 198-217. https://doi.org/10.11648/j.ajaf.20251304.14
Muhaimeed, A. S., Nassar, M., & Katalan, B. (2025b). The impact of climate change on moisture balance and land degradation in the region of Southern Iraq. International Journal of Agriculture Innovations and Research, 13(6), 118-136. https://www.ijair.org/issues.php?id=13-6-2025#10
National Oceanic and Atmospheric Administration. (2020). Drought report. National Centers for Environmental Information. https://www.ncei.noaa.gov/access/monitoring/monthly-report/drought/2020
Panagoulia, D., & Dimou, G. (1998). Definitions and effects of droughts. In Conference on Mediterranean Water Policy: Building on Existing Experience, Mediterranean Water Network. https://www.researchgate.net/publication/273740000_DEFINITIONS_AND_EFFECTS_OF_DROUGHTS
Paniagua, M. T., Villalba, J., & Pasten, M. (2020). Spatial-temporal distribution of drought in the western region of Paraguay (2005-2017). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3W12-2020), 327-330. https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-327-2020
Renza, D., Martinez, E., Arquero, A., & Sanchez, J. (2010). Drought estimation maps by means of multidate Landsat fused images. In Proceedings of the 30th EARSeL Symposium (pp. 775-782). https://www.academia.edu/download/105493185/EARSeL-Symposium-2010_17-03.pdf
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, 1, 309-317.
Salas-Martinez, F., Valdes-Rodriguez, O. A., Palacios-Wassenaar, O. M., Marquez-Grajales, A., & Rodriguez-Hernandez, L. D. (2023). Methodological estimation to quantify drought intensity based on the NDDI index with Landsat 8 multispectral images in the central zone of the Gulf of Mexico. Frontiers in Earth Science, 11, 1027483. https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1027483/full
Sharifi, A. (2020). Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran. Journal of the Indian Society of Remote Sensing, 48(9), 1289-1296. https://doi.org/10.1007/s12524-020-01155-y
Tavazohi, E., & Ahmadi Nadoushan, M. (2018). Assessment of drought in the Zayandehroud basin during 2000-2015 using NDDI and SPI indices. Fresenius Environmental Bulletin. https://doi.org/10.46944/feb.18.27.4.2332-2340
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://www.sciencedirect.com/science/article/pii/0034425779900130
United Nations Development Programme. (2022). Institutional survey & assessment report on drought early warning system in Iraq. https://www.undp.org/iraq/publications/institutional-survey-assessment-report-drought-early-warning-system-iraq
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