Monitoring agricultural drought in Iran using time series of vegetation health index

Document Type : Original Research Article


1 Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti, Tehran, Iran

2 Department of Physical Geography, Faculty of Geography, Tehran University, Tehran, Iran


In this study, in order to examine the severity of drought risk in Iran during 18-year period (2001-2018), we used rain meteorological data of 176 synoptic meteorological stations, ground surface temperature product (MOD11C3) and vegetation product (MOD13C2) of Moderate Resolution Imaging Spectro radiometer (MODIS). The study results show that the highest drought severity occurred in 2001 and 2003, in 2001 in spring 85.8% and in the summer of 2003, 93.8% of the country was affected by different degrees of drought. During the 18-year period, the highest percentage of drought was related to mean (45.3%), mild (25%), and severe (8.9%) classes, and no drought (20.79%), respectively. The results indicate that in the whole country, there is no severe drought region in the studied period. According to the map of the rainy regions, Iran's rainfall is highly dependent on the roughness, latitude and frequency of the arrival of synoptic systems in the region. The spatial rainfall map in Iran is well suited to VHI drought maps. The regions that receive less than 100 mm of rainfall represent the real deserts of the country. In these regions, severe drought conditions occur. The rainy regions between 100 and 200 mm, in terms of drought conditions, are in the mean class of drought. The rainy regions with a range of 200 to 300 mm are in the mild class of drought and rainy regions with more than 300 mm are usually not affected by drought stress.


Main Subjects

Bhuiyan, C., 2004. various droughts for monitoring drought condition in Aravalli terrain of India. In Proceedings of the XXth ISPRS Conference.Int. Soc. Photogramm. Remote Sensing, Istanbul.
Cammalleria, C., Verger, A.R., Lacazec, R. & Vogta, J.V., 2019. Harmonization of GEOV2 fAPAR time series through MODIS data for global drought monitoring. International Journal of Applied Earth Observation and Geoinformation, 80, 1-12.
Caccamo, A., Majumder, S., Richardson, A., Strong, R. & Oddo, S., 2011. Molecular interplay between mammalian target of rapamycin (mTOR), amyloid-b, and Tau: effects on cognitive impairments. Journal of Biological Chemistry, 285, 13107-1320.
Funk, C. & Budd, M.E., 2009. Phenologically-Tuned MODIS NDVI-based production nomaly estimates for Zimbabwe. Remote Sensing of Environment, 113 p.
Garcia-Leon, D., Contrerasc, S. & Huninkc, J., 2019. Comparison of meteorological and satellite-based drought indices as yield predictors of Spanish cereals. Agricultural Water Managemen, 213, 388-396.
Gouveia, C., Trigo, R.M. & DaCamra, C.C., 2009. Drought and vegetation stress monitoring in Portugal using satellite data. Natural Hazards and Earth System Sciences, 9(1), 185-195.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G., 2002. Remote Sensing for Natural Resources Management and Enviromental Monitoring: Manual of remote sensing3 ed. V. 4, Univercity of Arizona.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195-213.
IPCC, 2007. Climate change- synthesis report. Fourth Assessment Report of the Intergovernmental Panel of Climate Change. Rome.
JIE, Z., Mu, Q. & Hu, J., 2016. Assessing the remotely sensed Drought Severity Index for agricultural drought monitoring and impact analysis in North China. Ecological Indicators, 63, 296-309.
Kogan, F.N., 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8), 1405-1419.
Kogan, F.N., 2001. Operational space technology for global vegetation assessment. Bulletin of the American Meterological Society. 82(9), 64-1949.
Kogan, F.N., 2002. World droughts in the new millenium from AVHRR-based Vegetation Health Indices. Eos Transaction of American Geophysical Union, 83(48), 562-563.
Liu, C.L. & Wu, J.J., 2008. Crop drought monitoring using MODIS NDVI over Mid- Territory of China. International Geosciense and Remote Sensing Symposium. DOI: 10.1109/IGARSS.2008.4779491.
Martha, C.A., Cornelio, A.Z., pol, C.S., Christopher, R.H., Kathryn, S.M., Tugrul, Y.F., Jason, A.O. & Robert, T., 2016. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts, Remote Sensing of Environment, 174, 82-99.
Rojas, O., Vrieling, A. & Rembold, F., 2011. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment, 115, 343-52.
Rizqi, I., Bambang, H.T., Diar, S., La Ode, S.I., Selamet, K.M. & Dyah, R.P., 2016. Identification of agricultural drought extent based on vegetation health indices of Landsat data: case of Subang and Karawang. Indonesia. Procedia Environmental Sciences,33, 14-20.
Rizky Auliaa, M., Liyantonoa Setiawanb, Y. & Fatikhunnadaa, A., 2016. Drought detection of West Java’s paddy field using MODIS EVI satellite images (case study: Rancaekek and Rancaekek Wetan). Procedia Environmental Sciences, 33, 646-653.
Sharma, A., 2006. Spatial data mining for drought monitoring: An approach using temporal NDVI and rainfall relationship. Thesis Geo- Information Science and Earth Observation, India.
Sruthi, S. & Mohammed Aslam, M.A., 2015. Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. Aquatic Procedia, 4, 1258-1264.
Tallaksen, L.M. & Van Lanen, H.A., 2004. Hydrological drought: processes and estimation methods for streamflow and groundwater. Elsevier, 48, 22.
Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Legg, C., Jean, D. & Dieu, M., 2004. The Use of Remote Sensing Data for Drought Assessment and Monitoring in Southwest Asia. International Water Management Institute, PO Box 2075, Colombo, Sri Lanka.
Vicente-Serrano, S.M., Cuadrat-Prats, J.M. & Romo, A., 2006. Early prediction of crop production using drought indices at different time-scales and remote sensing data: application in the Ebro valley (North-East Spain). International Journal of Remote Sensing, 27(3).
Vyas, S.S., Bhattacharya, B.K., Nigam, R., Guhathakurta, P., Ghosh, K., Chattopadhyay, N. & Gairola, R.M., 2015. A combined deficit index for regional agricultural drought assessment over semi. arid tract of India using geostationary meteorological satellite data. International Journal of Applied Earth Observation and Geoinformation, 39, 28-39.
Zhang, A. & Jia, G., 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment, 134, 1223.