Iranian Water Research Journal

Iranian Water Research Journal

Monitoring of Agricultural and Meteorological Drought Using Satellite Data in Chaharmahal and Bakhtiari Province

Document Type : Original Article

Authors
1 Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
2 Department of Water Engineering, Faculty of Agriculture, Shahrekord. University, Shahrekord, Iran
3 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
Abstract
Introduction

Drought is one of the most important climate hazards that has widespread impacts on water resources, agriculture and ecosystems. Monitoring and assessing this phenomenon requires the use of valid indicators and reliable data. Preventive measures and planning against drought are of great importance in reducing its effects, which requires the use of sufficient knowledge in drought forecasting. Recently, remote sensing and techniques developed based on satellite images have been able to provide appropriate estimates of drought on a regional scale. Currently, satellite images are regularly obtained from the Earth's surface with high spatial resolution and can provide valuable spatial data. The advantages of using remote sensing over meteorological methods include increased sampling points, wider coverage area, higher temporal resolution and lower cost.

Using drought indices based on remote sensing data, it is possible to examine spatial patterns of drought. However, these indices are geographically or temporally specific and their accuracy decreases when used in other regions and times. In this regard, the main objective of this study is to investigate the spatial and temporal distribution patterns of drought and determine the performance of remote sensing indices in the spring and summer seasons in Chaharmahal and Bakhtiari province.

Material and Methods

Chaharmahal and Bakhtiari Province plays a key role in providing the country's water resources, as it is the headwaters of the Karun and Zayandeh Rood rivers. This makes accurate and timely monitoring of drought in the province essential. This study, along with validating satellite image precipitation data, examines the correlation of each satellite index with the Standardized Precipitation Index (SPI), a global standard index for drought assessment. In this study, a set of indices based on remote sensing data, including the Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Plant Health Index (VHI), was used to assess the drought situation in Chaharmahal and Bakhtiari Province. These indices were calculated using MODIS sensor images on a seasonal scale and for the period 2000 to 2023 for 33 stations. In addition, evaluated IMERG satellite precipitation data were also used. The IMERG data is a satellite precipitation dataset with a spatial resolution of 0.1°, produced by combining data from the TRMM and GPM satellites

Results and Discussion

The results of the assessment of the accuracy of IMERG precipitation data showed that the correlation of these data is high, between 0.83 and 0.96. These results indicate that IMERG precipitation satellite data can be used as an acceptable source for drought monitoring. The results also showed that, based on the spring VCI index, which is highly sensitive to changes in vegetation cover, the year 2000 was the driest year, with about 78 percent of the province's area affected by drought, while in 2020, about 88 percent of the province was without drought. The TCI index, which mainly reflects temperature conditions and heat stress, showed that in years with adequate precipitation, the index value increased due to a decrease in land surface temperature (such as 2019). In contrast, in dry years such as 2008 and 2021, TCI values decreased, indicating an intensification of the effect of drought through increased heat stress.The quarterly SPI index in spring showed the highest significant correlation with the TCI, VHI and VCI indices (0.85, 0.81 and 0.65, respectively), indicating the effect of temperature and short-term precipitation in determining the health and greenness of vegetation in spring. In summer, the highest correlation was related to the twelve-month and six-month SPI with the VCI index because the effect of precipitation on vegetation appears with a time lag. Therefore, TCI and VHI indices in spring and VCI in summer can provide a more accurate and complete picture of the vegetation response to spring precipitation in the province.

Conclusion

One of the important findings of this study is that the northern and eastern regions of the province are more vulnerable to drought. This finding can help provincial decision-makers prioritize areas of the province in terms of allocating financial resources to implement drought adaptation methods or preparing to manage farmers' protests. These areas can also be prioritized for water resource allocation or intra-provincial water transfer projects. In addition, these areas can be prioritized for drought resilience projects, such as planning for appropriate crop timing, changing cropping patterns, selecting drought-resistant species and modifying or replacing irrigation methods.
Keywords
Subjects

1.    Arabi, Z. and badraghnejad, A., 2021. Correlation Analysis of Drought Time Series Based on Modis Satellite Images and Standardized Precipitation Climate Index (SPI) on the eastern slope of Zagros. Journal of Spatial Analysis Enviromental Hazarts, 8 (4), pp. 71-88.https://doi.org/10.22034/gp.2021.44939.2803  [In Persian].
 
2.    Almamalachy, Y., 2017. Utilization of Remote Sensing in Drought Monitoring Over Iraq. PhD thesis, Portland State University, Portland.
 
3.    Aydin, M., 2025. Evaluation of the Usability of the Rainfall Anomaly Index (RAI) Instead of the Standard Precipitation Index (SPI). Iranian Journal of Science and Technology, Transactions of Civil Engineering, 49(1), pp. 763-785. https://doi.org/10.1007/s40996-024-01516-4 .
 
4.    Babaei, E., Asadi Zarch, M.A., Hosseini, S.Z. and Shahmoradi, S., 2025. Performance evaluation of composite remote sensing indices in drought assessment (case study: Chaharmahal and Bakhtiari Province, Iran). Journal of Natural Environmental Hazards, 14(43), pp. 155-180. https://doi.org/10.22111/jneh.2024.49797.2067.  [In Persian].
 
5.    Bhuiyan, C., Saha, A. K., Bandyopadhyay, N. and Kogan, F.N., 2017. Analyzing the impact of thermal stress on vegetation health and agricultural drought–a case study from Gujarat, India. GIScience & Remote Sensing, 54(5), pp. 678-699. https://doi.org/10.1080/15481603.2017.1309737
 
6.    Chen, S., L. Zhang, Y. Zhang, M. and X. Liu., 2020. Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin, China. Journal of Geographical Sciences, 30(1), pp. 53-67. https://doi.org/10.1007/s11442-020-1714-y
 
7.    Degerli, S. and Turhan, E., 2025. An evaluation of spatiotemporal changes of meteorological drought in the Mediterranean sub-basins in Türkiye using discrepancy precipitation and standardized precipitation index. Natural Hazards, 121(2), pp. 2293-2322. https://ideas.repec.org/a/spr/nathaz/v121y2025i2d10.1007_s11069-024-06906-5.html
 
8.    Guttman, N.B., 1999. Accepting the standardized precipitation index: a calculation algorithm. JAWRA Journal of the American Water Resources Association, 35(2), pp. 311-322. https://doi.org/10.1111/j.17521688.1999.tb03592.x
 
9.    Hamzeh, S., Farahani, Z., Mahdavi, Sh., Chatrabgoun, A. and Gholamnia, M., 2017. Spatio-temporal monitoring of agricultural drought using remote sensing data: Case study of Markazi Province, Iran. Spatial Analysis of Environmental Hazards, 4(3), pp. 53-70. [In Persian].
 
10. Hao, C., Zhang, J. and Yao, F., (2015). Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation 35, pp. 270-283.
 
11.               Hayes, M., Svoboda, M., Wall, N. and Widhalm, M., 2011. The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bulletin of the American Meteorological Society, 92(4), pp.485-488. https://doi.org/10.1175/2010BAMS3103.1
 
12. Huffman, G.J., Bolvin, D.T., Nelkin, E.J. and Tan, J., 2015. Integrated multi-satellite retrievals for GPM (IMERG) technical documentation. Nasa/Gsfc Code, 612(47), 2019.
 
13. Huang, J., Zhuo, W., Li, Y., Huang, R., Sedano, F., Su, W. and Zhang, X., 2020. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. International Journal of Digital Earth, 13 (4), 504–526. https://doi.org/10.1080/17538947.2018.1542040
 
14. Kogan, F.N., (1995). Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11), pp. 91-100. https://doi.org/10.1016/0273-1177(95)00079-T
 
15. Kogan, F., B. Yang, G. Wei, P. and Xianfeng, J., 2005. Modelling corn production in China using AVHRR-based vegetation health indices. International Journal of Remote Sensing. 26(11), 2325–2336.
 
16. Li, M., Wang, P., Tansey, K., Sun, Y., Guo, F. and Zhou, J., 2025. Improved field-scale drought monitoring using MODIS and Sentinel-2 data for vegetation temperature condition index generation through a fusion framework. Computers and Electronics in Agriculture, 234, 110256.
 
17. Liu, Q., Zhang, S., Zhang, H., Bai, Y. and Zhang, J., 2020. Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment, 711, 134585.
 
18. McKee, T. B., Doesken, N. J. and Kleist, J., 1993. The relationship of drought frequency and duration to time scales, In Proceedings of the 8th Conference on Applied Climatology, Anaheim, 17(22), pp. 179-183.
 
19. Mtilatila, L., Bronstert, A., Bürger, G. and Vormoor, K., 2020. Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970–2013). Hydrological Sciences Journal, 65(16), pp.2750-2764. https://doi.org/10.1080/02626667.2020.1837384
 
20. Nawabi, N., Maghdesi, M. and Ganji, N., 2019. Assessment of agricultural drought monitoring using various indices based on ground and remote sensing data: Case study of Lake Urmia watershed. Journal of Watershed Engineering and Management, 13(1), pp. 1–12. [in Persian].
 
21. Salimi, M., Sanaeinezhad, S, H., Sepehr, A. and Sabet, L., 2018. Drought monitoring based on satellite index (SDI) and TRMM data. (Case Study; Khorasan Razavi province. Nivar, 42(102), pp. 19–30. [in Persian].
 
22. Tafi, Sh., Baladi, A., Soltani, A. and Pighan, Kh., 2021.Comparison and Evaluation of Estimating Reference Evapotranspiration Methods in Three General Categories Based onTemperature, Radiation and Mass Transfer (Case Study: Lorestan Province). Nivar, 44(110), pp. 107-120. [in Persian].
 
23. Vahidi, S., Amini, AS. and Hatamzadeh, V., 2023. Remote Sensing Indexes Assessment for Drought Monitoring Using Sentinel Satellite Imagery: A Case Study from Natanz County, Iran. Asian Journal of Geographical Research, 6(1), pp. 35-43.https://doi.org/10.9734/ajgr/2023/v6i1175
 
24. Wei, W., Zhang, J., Zhou, L., Xie, B., Zhou, J. and Li, C., 2021. Comparative evaluation of drought indices for monitoring drought based on remote sensing data. Environmental Science and Pollution Research, 28 (16), pp. 20408–20425. https://doi.org/10.1080/01431160500034235
 
25. Wilhite, D., 2000. Chapter 1. Drought as a natural hazard: concepts and definitions. In: Donald A. Wilhite (Ed.), Drought Mitigation Center Faculty, a global assessment. Vol. I. London (UK): Routledge; pp. 3–18.
 
26. Zhang, L., Jiao, W., Zhang, H., Huang, C. and Tong, Q., 2017. Studying drought phenomena in the continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment, 190, pp. 96–106. https://doi.org/10.1016/j.rse.2016.12.010
 
27. Zhang, Y., Liu, X., Jiao, W., Zeng, X., Xing, X., Zhang, L. and Hong, Y., 2021. Drought monitoring based on a new combined remote sensing index across the transitional area between humid and arid regions in China. Atmospheric Research, 264, pp. 105850. https://doi.org/10.1016/j.atmosres.2021.105850
 
28. Zhao, X., Xia, H., Liu, B. and Jiao, W., 2022. Spatiotemporal comparison of drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 using various drought indices in Google Earth Engine. Remote Sensing, 14, pp. 1570. https://doi.org/10.3390/rs14071570
 
29. Zhong, S., Sun, Z. and Di, L., 2021. Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data. Ecological Indicators, 127, pp. 107767. https://doi.org/10.1016/j.atmosres.2021.105850

  • Receive Date 15 December 2025
  • Revise Date 21 January 2026
  • Accept Date 27 January 2026
  • Publish Date 21 March 2026