Journal of Range and Watershed Managment

Current Issue

By Issue

By Author

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Journal Metric

Reviewers

Guide for Reviewers

Superior reviewers

Evaluation of Remote-Sensing-Based Indices for Drought Monitoring Using MODIS Images (Case Study: Bam Plain)

Document Type : Research Paper

Authors

1 Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran

2 Department of Rangeland and Watershed Management, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd, Iran

3 Department of Water Engineering, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

10.22059/jrwm.2025.393588.1821

Abstract

Since that the traditional methods are based on meteorological station data and more investigate meteorological drought, hence, the application of remote sensing techniques and satellite images have been considered as a useful tool for monitoring of agricultural drought. In this study, the relationship between the meteorological drought index (SPI) and remote-sensing-based indices (VCI, TCI and VHI) has been studied in Bam plain. In this regard, using Terra satellite images (Modis sensor) and precipitation data of synoptic and rain gauge stations in the study area, the occurred changes were detected over the 15-year period. In this study, with respect to high temporal accuracy and spectral coverage and accessibility, MOD13A3 and MOD11A1 products extracted from MODIS sensor were used during 2009 to 2023. Then, indices of VCI, TCI and VHI were compared with Standard Precipitation Index (SPI). The results of drought mapping with the SPI index during 2010 to 2024 showed that the drought severity has increased from north to south in the study area. So that, extremely drought is detected in the southern regions of the plain and extremely wet is detected in the northern regions. The annual correlation coefficient between SPI, VCI and SPI indices is 0.70 and 0.53, respectively. It shows high significant positive correlation at level of 0.05. The correlation coefficient between SPI and TCI is 0.11 which shows a weak correlation but significant positive at level of 0.05. Therefore, VCI and VHI Indices have more correlation with annual precipitation and have acceptable results compared with TCI Index.

Keywords

References
Arekhi, S., Barzegar Savasari, M., & Emadaddian, S. (2022). Investigating the Indicators Resulting from Remote Sensing Technology in Drought Assessment using MODIS Images (Case Study: Qom, Isfahan, Chaharmahal and Bakhtiari, and Markazi Provinces). Journal of Geography and Environmental Hazards, 11(3), 189-224. (In Persian)
Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G., & Sutera, A. (2003). Spatial variability of drought: An analysis of the SPI in Sicily. Water Resources Management, 17: 273–296.
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Gregoire, J.M. (2001). Detecting vegetation leaf water content using relfectance in the optical domain. Remote Sensing of Environment,77:22-33.
Dutta, D., Kundu, A., Patel, N. R., Saha, S. K., & Siddiqui, A. R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science, 18(1), 53-63.
Ghafourian, H., Sana’inezhad, S.H., & Davari, K. (2014). Study on determining suitable areas for drought monitoring using TRMM satellite data (Case study: Khorasan Razavi Province (water and soil). Agricultural Sciences and Industries. 3: 639-648. (In Persian).
Goddard, L., Mason, S.J., Zebiak, S.E., Ropelewski, C.F., Basher, R., & Cane, M.A. (2001). Current approaches to seasonal to inter annual climate predictions. International Journal of Climatology. 21: 1111–1152.
Hamzeh, S., Farahani, Z., Mahdavi, Sh., Chaterabgun, O., & Gholamnia, M. (2017). Temporal and spatial monitoring of agricultural drought using remote sensing data, case study: Markazi province of Iran. Journal of Spatial Analysis of Environmental Hazards. 4(3), 53-70. (In Persian).
Heim, RR. (2002). A review of twentieth-century drought indices used in United -States. Bulletin of the American Meteorological Society, 84, 1149-1165.
Hodel, E. (2012). Analysing Land Cover Change in Mongolia Using Terra MODIS Satellite Data supervisor Hans Hurni, Masterarbeit der Philosophisch, Universität Bern. https://www.scirp.org/journal/paperinformation.aspx?paperid=4981
Jahanbakhsh, S., Hadiani, M., Rezaei Banafsheh, M., & Dinpajouh, Y. (2010). Modeling Climate Change Parameters in Mazandaran Province. Proceedings of the Fourth International Congress of Geographers of the Islamic World. Pp 13.
Jain, S. K., Keshri, R., Goswami, A., & Sarkar, A. (2010). Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Natural hazards, 54, 643-656.
Ji, L., & Peters, A.J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87, 85-98. https:// www.sciencedirect.com/ science/ article/ abs/pii/S0034425703001743.
Jiang, W., Wang, L., Feng, L., Zhang, M., & Yao, R. (2020). Drought characteristics and its impact on changes in surface vegetation from 1981 to 2015 in the Yangtze River Basin, China. International Journal of Climatology, 40(7), 3380-3397.
Karnieli, A., Bayasgalan, M., Bayarjargal, Y., Agam, N., Khudulmur. S., & Tucker, CJ. (2006). Comments on the use of the Vegetation Health Index over Mongolia. International Journal of Remote Sensing, 27(10): 2017-2024.
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15 (11): 91-100.
Kogan, F.N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78(4), 621-636.
Kogan, F.N. (2000). Global drought detection and impact: Assessment from apace, In Wilhite Editor Drought a Global Assessment, 1: 197-206.
Kogan, F., Guo, W., & Yang, W. (2017). SNPP/VIIRS vegetation health to assess 500 California drought. Geomatics, Natural Hazards and Risk, 8(2), 1383-1395.
McKee, T.B., N.J. Doesken & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17(22): 179-183.
Pishnamaz Ahmadi, M., Houshmand, A., & Zamani, F. (2017). Drought monitoring in the northwest of the country using TRMM and MODIS data. 24th National Geomatics Conference, May 2017. (In Persian).
Quiring, S. M., & Ganesh, S. (2010). Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agricultural and forest meteorology, 150(3), 330-339.
Rahimzadeh, P. (2005). Investigating the possibility of using NOAA-AVHRR images for drought monitoring. Master's thesis, University of Tehran, Faculty of Environment. (In Persian).
Rezaei Moghadam, M.H., Valizadeh, K., Rostamzadeh, H., & Rezaei, A. (2012). Evaluating the efficiency of MODIS sensor data in drought estimation (case study: Lake Urmia watershed). Journal of Geography and Environmental Sustainability, 3, 1-16. (In Persian).
Rhee, J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114, 2875–2887.
Rostami, A., Bzane, M., & Raini, M. (2016). Spatial and temporal monitoring of agricultural drought using Modis imagery and remote sensing technology. Journal of Soil and Water Science, 27, 213-226. (In Persian).
Rostami, F., Attarod, P., Keshtkar, H., & Nazeri Tahroudi, M. (2022). Impact of climatic parameters on the extent of mangrove forests of southern Iran. Caspian Journal of Environmental Sciences, 20(4), 671-682.
Rouse, J.W.; Haas, Jr. R.; Schell, H. J. A.; & Deering, D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA: 309-317.
Shamsipour, A., Alavipanah, K., & Mohammadi, H. (2010). Studying the effectiveness of NOAA-AVHRR satellite vegetation and thermal indixes in drought analysis of Kashan region. Iranian Scientific and Research Journal of Rangeland and Desert Research, 17(3), 445 - 465. (In Persian).
Shareful Hassan, M., & Mahmud-ul-islam, S. (2013). Drought Vulnerability Assessment in the High Barind Tract of Bangladesh Using MODIS NDVI and Land Surface Temperature (LST) Imageries. International Journal of Science and Research (IJSR) ISSN (Online), 2319-7064.
Shojaee Shiri, H., Arzani, H., Keshtkar, H., Bagheri, S. & Kavoosi, O. (2025). Assessment and Monitoring of Drought Using Satellite and Meteorological Time Series Data (Case Study: Zanjan Province). Journal of Ecohydrology, 12(1), 613-634. (In Persian)
Sultani, M., Soltani, A., Kalehhoi, M., & Soleimani, K. (2019). Regional drought monitoring using Landsat images, case study: Kermanshah city. Geographic Information Quarterly (Sepehr), 28(109), 138-146. (In Persian).
Thenkabail, P.S., Gamage, M.S.D.N., & Smakhtin, V.U. (2003). The Use of Remote Sensing Data for Drought Assessment and Monitoring in Southwest Asia. Research report. 85, 1-34.
Tsakiris, G.; & Vangelis, H. (2004). Towards a drought watch system based on spatial SPI. Water Resources Management, 18, 1-12.
Zamanian, M.T., Behyar, M.B., Hosseini Karimi, A. & Vazife Dust M. (2016). Monitoring and analysis of drought using NOAA-AVHRR satellite sensing products 2012. Journal of Climatological Research, 9(3), 33-54.
Zambrano, F., Lillo-Saavedra, M., Verbist, K., & Lagos, O. (2016). Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sensing, 8(6), 530-549.
Zhang, M., Zhu, X., Fan, J., Li, G., & Zhang Y. (2009). Monitoring drought dynamics in Huanghuai region of China using AVHRR-based vegetation health indices in comparison with ground data. Remote Sensing for Agriculture, Ecosystems, and Hydrology XI. 72-74. Proc of SPIE Edited by Christopher M. U. Neale, Antonino Maltese.
Journal of Range and Watershed Managment
Volume 78, Issue 4
January 2026
Pages 471-489
Files
History
  • Receive Date: 16 April 2025
  • Revise Date: 13 May 2025
  • Accept Date: 18 May 2025
Share
How to cite
Statistics