Azeez, M., Al Sharaa, H., Ziboon, A. (2025). Mapping agricultural lands at risk of meteorological drought in iraq using geostatistics. , 43(5), 351-364. doi: 10.30684/etj.2025.155327.1855
Mohammed H. Azeez; Hisham M. Jawad Al Sharaa; Abdul Razzak T. Ziboon. "Mapping agricultural lands at risk of meteorological drought in iraq using geostatistics". , 43, 5, 2025, 351-364. doi: 10.30684/etj.2025.155327.1855
Azeez, M., Al Sharaa, H., Ziboon, A. (2025). 'Mapping agricultural lands at risk of meteorological drought in iraq using geostatistics', , 43(5), pp. 351-364. doi: 10.30684/etj.2025.155327.1855
Azeez, M., Al Sharaa, H., Ziboon, A. Mapping agricultural lands at risk of meteorological drought in iraq using geostatistics. , 2025; 43(5): 351-364. doi: 10.30684/etj.2025.155327.1855

Mapping agricultural lands at risk of meteorological drought in iraq using geostatistics

Engineering and Technology Journal
Volume 43, Issue 5, May 2025, Pages 351-364    PDF (2.3 M)
Document Type: Research Paper
DOI: 10.30684/etj.2025.155327.1855
Authors
Mohammed H. Azeez* 1; Hisham M. Jawad Al Sharaa2; Abdul Razzak T. Ziboon3
1Surveying Dept., Shatra Technical Institute (STI), Southern Technical University (STU), Alshtra, Iraq. Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
2Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
3College of Engineering, Al-Esraa University, Baghdad, Iraq.
Abstract
Identifying vulnerable agricultural lands at risk of meteorological drought is challenging for researchers. Its complexity lies in the fact that agricultural lands are irrigated by two sources: rain and rivers. In this research, we have developed a geostatistical model to separate river-dependent lands unaffected by meteorological drought waves and land vulnerable to the risk of meteorological drought. The inputs of this geostatistical model are the vegetation index and the humidity index extracted from Landsat 8, the rainfall from CHIRPS, and the LULC from ESA. The correlation between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the rainfall was tested using the Pearson Correlation Coefficient (PCC) for more than five million pixels representing agricultural lands in Dhi-Qar Iraq. The Getis-Ord Gi* statistical index was used to cluster each pixel according to PCC value. This model achieved accurate results as it was validated using ground truth and the BEAST (Bayesian Estimator of Abrupt Change, Seasonality, and Trend) model, and the results of this model were promising. It was concluded that 42% of the lands in the study area are vulnerable to the risk of meteorological drought, rivers permanently irrigate 37%, and 21% are cultivated in the winter season only.

Highlights
  • Create a model to identify areas that use river irrigation and those that use seasonal irrigation.
  • An overall methodology identified vulnerable agricultural areas at risk of meteorological drought in time and space.
  • The BEAST model performed well in time series analysis, making the geostatistical model applicable to all land covers
  • The geostatistical model is applicable globally at the pixel level for comprehensive analysis.
Keywords
Drought; vulnerability; land degradation; spatial modeling; cropland
References
  1. SHARMA, Jagmohan; RAVINDRANATH, Nijavalli H. Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change. Environ. Res. Commun., 1 (2019) 1-7.‏ https://dx.doi.org/10.1088/2515-7620/ab24ed
  2. H. Guo, X. Wen, Y. Wu, J. Wang, and Q. Liang, Drought risk assessment of farmers considering their planting behaviors and awareness: A case study of a County from China, Ecol. Indic., 137 (2022) 108728. https://doi.org/10.1016/j.ecolind.2022.108728
  3. R. S. Dwivedi, Geospatial technologies for land degradation assessment and management. CRC. Press., 2018.
  4. R. Prăvălie, Exploring the multiple land degradation pathways across the planet, Earth Sci. Rev., 220 (2021) 103689. https://doi.org/10.1016/j.earscirev.2021.103689
  5. I. Alwan, A. R. Ziboon, and A. Khalaf, Monitoring of Agricultural Drought in the Middle Euphrates Area, IraqUsing Landsat Dataset, Eng. Technol. J., 37 (2019) 222-226. https://doi.org/10.30684/etj.37.7A.1
  6. D. Ge, X. Gao, and W. Xinguang, Changes in spatiotemporal drought characteristics from 1961 to 2017 in northeastern maize-growing regions, China, Irrig. Sci., 42 (2024) 163-177. https://doi.org/10.1007/s00271-023-00893-4
  7. A. Fathi, H. Shafizadeh-Moghadam, M. Minaei, and T. Xu, Influence of drought duration and severity on drought recovery period for different land cover types: evaluation using MODIS-based indices, Ecol. Indic., 141 (2022) 109146. https://doi.org/10.1016/j.ecolind.2022.109146
  8. A. A. Belal, H. R. El-Ramady, E. S. Mohamed, and A. M. Saleh, Drought risk assessment using remote sensing and GIS techniques, Arabian J. Geosci., 7 (2014) 35-53. https://doi.org/10.1007/s12517-012-0707-2
  9. V. Sehgal, N. Gaur, and B. Mohanty, Global Flash Drought Monitoring Using Surface Soil Moisture, Water Resour. Res., 57 (2021) 1-25. https://doi.org/10.1029/2021WR029901
  10. S. Chen, T. Y. Gan, X. Tan, D. Shao, & J. Zhu, Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China, Clim. Dyn., 53 (2019) 737-757. https://doi.org/10.1007/s00382-018-04611-1
  11. R. Albarakat, M.-H. Le, and V. Lakshmi, Assessment of drought conditions over Iraqi transboundary rivers using FLDAS and satellite datasets, J. Hydrol Reg. Stud., 41 (2022) 101075. https://doi.org/10.1016/j.ejrh.2022.101075
  12. Y. Tramblay, A.Koutroulis, L.Samaniego, S. M. Vicente-Serrano, F. Volaire,  et al., Challenges for drought assessment in the Mediterranean region under future climate scenarios, Earth Sci. Rev., 210 (2020) 103348. https://doi.org/10.1016/j.earscirev.2020.103348
  13. P. V. V Le, T. Phan-Van, K. V Mai, & D. Q. Tran, Space–time variability of drought over Vietnam, Int. J. Climatol., 39 (2019) 5437-5451. https://doi.org/10.1002/joc.6164
  14. I. Hatem, I. A. Alwan, A. R. T. Ziboon, and A. Kuriqi, Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery, Open Engineering, 14 (2024) 20220583. https://doi.org/10.1515/eng-2022-0583
  15. Y. S. Almamalachy, A. M. F. Al-Quraishi, and H. Moradkhani. 2020. Agricultural drought monitoring over Iraq utilizing MODIS products, Environmental remote sensing and GIS in Iraq, pp. 253-278. Springer Cham. https://doi.org/10.1007/978-3-030-21344-2_11
  16. L. Said, N. Aziz, and L. Y. H. Al-Soudany, Spatiotemporal Mapping of Agricultural and Meteorological Drought in Wasit Province Based on GIS and Remote Sensing Data, Ecol. Eng. Environ. Technol., 25 (2024) 115-122. https://doi.org/10.12912/27197050/186869
  17. Z. T. Abdulrazzaq, R. H. Hasan, and N. A. Aziz, Integrated TRMM data and standardized precipitation index to monitor the meteorological drought, Civ. Eng. J., 5 (2019) 1590-1598.
  18. Touma, Danielle, Martinez, Carlos National Center for Atmospheric Research Staff (Eds). Last modified 2023-08-02 "The Climate Data Guide: CHIRPS: Climate Hazards InfraRed Precipitation with Station data (version 2).
  19. C. Funk, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, et al., The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, Sci. Data, 2 (2015) 150066. https://doi.org/10.1038/sdata.2015.66
  20. Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., (2022). ESA WorldCover 10 m 2021 v200.
  21. K. Aryal, A. Apan, and T. Maraseni, Comparing global and local land cover maps for ecosystem management in the Himalayas, Remote Sens. Appl., 30 (2023) 100952. https://doi.org/10.1016/j.rsase.2023.100952
  22. T. Zhao, X. Zhang, Y. Gao, J. Mi, W. Liu, et al., Assessing the Accuracy and Consistency of Six Fine-Resolution Global Land Cover Products Using a Novel Stratified Random Sampling Validation Dataset, Remote. Sens. (Basel), 15 (2023) 2285. https://doi.org/10.3390/rs15092285
  23. M. Adnan Abdul Rahman, Characteristics of Rainfall in Dry and Wet Seasons in Iraq, Journal of the College of Education for Women, 33 (2022) 116-134. https://doi.org/10.36231/coedw.v33i1.1568
  24. M.-J. Fortin and M. Dale, Spatial analysis: a guide for ecologists. 2005. Accessed: Jun. 04, 2024. [Online]. Available: www.cambridge.org/9780521804349
  25. A. Liepa et al., Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa, Remote Sens. Appl., 35 (2024) 101230. https://doi.org/10.1016/j.rsase.2024.101230
  26. K. S. Meshesha, E. Shifaw, A. Y. Kassaye, M. A. Tsehayu, A. A. Eshetu, and H. Wondemagegnehu, Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia, Environ. Challenges, 15 (2024) 100895. https://doi.org/10.1016/j.envc.2024.100895
  27. R. M. Fokeng and Z. N. Fogwe, Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon, Geosyst. Geoenviron., 1 (2022) 100075. https://doi.org/10.1016/j.geogeo.2022.100075
  28. S. Paul et al., Assessment of endemic northern swamp deer (Rucervus duvaucelii duvaucelii) distribution and identification of priority conservation areas through modeling and field surveys across north India, Glob Ecol Conserv, 24 (2020) e01263. https://doi.org/10.1016/j.gecco.2020.e01263
  29. M. V Ferro, P. Catania, D. Miccichè, A. Pisciotta, M. Vallone, and S. Orlando, Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images, Biosyst Eng., 231 (2023) 36-56. https://doi.org/10.1016/j.biosystemseng.2023.06.001
  30. H. A. Feizabadi, A. Mohammadi, G. Shahnaseri, and H. Y. Wan, Comparing drivers and protection of core habitat and connectivity for two sympatric desert carnivores, Glob. Ecol. Conserv., 48 (2023) e02696. https://doi.org/10.1016/j.gecco.2023.e02696
  31. X. Hua, R. Ohlemüller, and P. Sirguey, Differential effects of topography on the timing of the growing season in mountainous grassland ecosystems, Environ. Adv., 8 (2022) 100234. https://doi.org/10.1016/j.envadv.2022.100234
  32. S. Sannigrahi, Q. Zhang, P.K. Joshi, P. C. Sutton, S. Keesstra, et al., Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region, J. Clean. Prod., 257 (2020) 120424. https://doi.org/10.1016/j.jclepro.2020.120424
  33. T. Zhang, C. Cheng, and S. Shen, Quantifying ecosystem quality in the Tibetan Plateau through a comprehensive assessment index, Environ. Sustainability Indic., 22 (2024) 100382. https://doi.org/10.1016/j.indic.2024.100382
  34. C. Wu and Z. Wang, Multi-scenario simulation and evaluation of the impacts of land use change on ecosystem service values in the Chishui River Basin of Guizhou Province, China, Ecol. Indic., 163 (2024) 112078. https://doi.org/10.1016/j.ecolind.2024.112078
  35. M. Luo, X. Jia, Y. Zhao, P. Zhang, and M. Zhao, Ecological vulnerability assessment and its driving force based on ecological zoning in the Loess Plateau, China, Ecol. Indic., 159 (2024) 111658. https://doi.org/10.1016/j.ecolind.2024.111658
  36. K. Yang , P. Zhou , J. Wu, Q. Yao, Z. Yang, Carbon stock inversion study of a carbon peaking pilot urban combining machine learning and Landsat images, Ecol. Indic., 159 (2024) 111657. https://doi.org/10.1016/j.ecolind.2024.111657
  37. B. Jasim, O. Jasim, and A. AL-Hameedawi, Monitoring Change Detection of Vegetation Vulnerability Using Hotspots Analysis, IIUM Eng. J., 25 (2024) 116–129. https://doi.org/10.31436/iiumej.v25i2.3030
  38. O. Aftergood and M. Flannigan, Identifying and analyzing spatial and temporal patterns of lightning-ignited wildfires in Western Canada from 1981 to 2018, Can. J. For. Res., 52 (2022). https://doi.org/10.1139/cjfr-2021-0353
  39. M. M. Yagoub and A. A. Al Yammahi, Spatial distribution of natural hazards and their proximity to heritage sites: Case of the United Arab Emirates, Int. J. Disaster Risk Reduct., 71 (2022) 102827. https://doi.org/10.1016/j.ijdrr.2022.102827
  40. R. Zhang, Y. Chen, X. Zhang, Q. Ma, and L. Ren, Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China, Int. J. Appl. Earth Obs. Geoinf., 108 (2022) 102717. https://doi.org/10.1016/j.jag.2022.102717
  41. J. Li, Z. Fang, J. Zhang, Q. Huang, and C. He, Mapping basin-scale supply-demand dynamics of flood regulation service – A case study in the Baiyangdian Lake Basin, China, Ecol. Indic., 139 (2022) 108902. https://doi.org/10.1016/j.ecolind.2022.108902
  42. Y. Zhang, J. Wua, S. Adilib, H. Zhangb, G. Shib, et al., Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China, Heliyon, 10 (2024) e28812. https://doi.org/10.1016/j.heliyon.2024.e28812
  43. A. Ndagijimana, G. Nduwayezuc, C. Kagoyirec, A. Umubyeyib, A. Mansourianc et al., Childhood stunting is highly clustered in Northern Province of Rwanda: A spatial analysis of a population-based study, Heliyon, 10 (2022) e24922.  https://doi.org/10.1016/j.heliyon.2024.e24922
  44. Z. Shi, D. Jiang, and Y. Wang, Spatiotemporal dependence of compound drought–heatwave and fire activity in China, Weather Clim Extrem, 45 (2024) 100695. https://doi.org/10.1016/j.wace.2024.100695
  45. B. Stumpe, B. Bechtel, J. Heil, C. Jörges, A. Jostmeier, et al., Soil texture mediates the surface cooling effect of urban and peri-urban green spaces during a drought period in the city area of Hamburg (Germany), Sci. Total Environ., 897 (2023) 165228. https://doi.org/10.1016/j.scitotenv.2023.165228
  46. A. Khoshnazar, G. Corzo, and M. Sajjad, Characterizing spatial–temporal drought risk heterogeneities: A hazard, vulnerability and resilience-based modeling, J. Hydrol, (2023) 129321. https://doi.org/10.1016/j.jhydrol.2023.129321
  47. K. Zhao, Michael A.Wulder, TongxiHu , Ryan Bright , Qiusheng Wu, et al., Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm, Remote Sens. Environ., 232 (2019) 111181.  https://doi.org/10.1016/j.rse.2019.04.034
  48. G. V. Laurin, A. Cotrina-Sanchez, L. Belelli-Marchesini, E. Tomelleri, G. Battipaglia, et al., Comparing ground below-canopy and satellite spectral data for an improved and integrated forest phenology monitoring system, Ecol. Indic., 158 (2024) 111328. https://doi.org/10.1016/j.ecolind.2023.111328
  49. Y. Ngadi Scarpetta, V. Lebourgeois, A.-E. Laques, M. Dieye, J. Bourgoin, and A. Bégué, BFASTm-L2, an unsupervised LULCC detection based on seasonal change detection – An application to large-scale land acquisitions in Senegal, Int. J. Appl. Earth Obs. Geoinf., 121 (2023) 103379. https://doi.org/10.1016/j.jag.2023.103379
  50. J. Mardian, A. Berg, and B. Daneshfar, Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis, Remote Sens. Environ., 255 (2021) 112292. https://doi.org/10.1016/j.rse.2021.112292
  51. J. Li, Z.-L. Li, H. Wu, and N. You, Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement, Remote Sens. Environ., 280 (2022) 113222. https://doi.org/10.1016/j.rse.2022.113222
  52. F. Di Nunno, G. de Marinis, and F. Granata, Analysis of SPI index trend variations in the United Kingdom - A cluster-based and bayesian ensemble algorithms approach, J. Hydrol. Reg. Stud., 52 (2024) 101717. https://doi.org/10.1016/j.ejrh.2024.101717
  53. S. Mehri, A. A. Alesheikh, and A. Lotfata, Abiotic factors impact on oak forest decline in Lorestan Province, Western Iran, Sci. Rep., 14 (2024) 3973. https://doi.org/10.1038/s41598-024-54551-6
  54. A. Ganji, M. Saeedi, M. Lloyd, J. Xu, S. Weichenthal, and M. Hatzopoulou, Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories, Sci. Total Environ., 915 (2024) 170075. https://doi.org/10.1016/j.scitotenv.2024.170075
  55. E. Saghbiny, L. Leblanc, A. Harlé; C. Bobbio; R. Vialle, et al., Breach Detection in Spine Surgery Based on Cutting Torque, IEEE Trans. Med. Robot. Bionics., 1 (2024) 1084-1092. https://doi.org/10.1109/TMRB.2024.3421543
  56. R. Lyu, J. Pang, J. Zhang, and J. Zhang, The impacts of disturbances on mountain ecosystem services: Insights from BEAST and Bayesian network, Appl. Geogr., 162 (2024) 103143. https://doi.org/10.1016/j.apgeog.2023.103143
  57. M. Sakizadeh, A. Milewski, and M. T. Sattari, Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran, Water (Basel), 15 (2023) 690. https://doi.org/10.3390/w15040690
  58. Tongxi Hu, Yang Li, Xuesong Zhang, Kaiguang Zhao, Jack Dongarra, and Cleve Moler, Package ‘Rbeast 1.23, 2023. https://pypi.org/project/Rbeast/
  59. S. Ali, Effect of irrigation water quality and nitrogen fertilizer in the growth of barley crop (hordeum vulgare l.) and some physical and chemical soil traits in dhi qar province, southern iraq, Plant Arch, 19 (2019) 2581-6063.
  60. A. Jabbar Khalaf Al meini, A Proposed Index of Water Quality Assessment for Irrigation, Eng. Technol. J., 28 (2010) 6557-6579. https://doi.org/10.30684/etj.28.22.9
  61. H. M. Hassan and H. S. Dakheel, Using the Normalized Difference Vegetation Index (NDVI) to study the change of vegetation cover in Thi-Qar Governorate, southern Iraq for the period from 1990-2022, Texas J. Agric. Biol. Sci., 13 (2023) 72-84.
  62. H. Kazim and A. Yassin, Detecting the state of drought and water deficit and its effects in Dhi Qar Governorate, Ibn Khaldun J. Stud. Res., 2 (2022) https://www.benkjournal.com/article/view/1080.
  63. N. Muhaisen, T. Khayyun, and M. Al-Mukhtar, Drought forecasting model for future climate change effects in a regional catchment area in northern Iraq, Eng. Technol. J., 42 (2024) 525–539. https://doi.org/10.30684/etj.2024.144458.1634
  64. R. Attafi, A. D. Boloorani, A. M Fadhil Al-Quraishi, and F. Amiraslani, Comparative analysis of NDVI and CHIRPS-based SPI to assess drought impacts on crop yield in Basrah Governorate, Iraq, Caspian J. Environ. Sci., 19 (2021) 547–557.
Statistics
Article View: 267
PDF Download: 150


Journal Management System. Powered by iJournalPro.com