Determination of An Optimized Multi-Sensor Remote Sensing Index to Promote Real-Time Drought Monitoring Over the Heterogeneous Land Covers

Document Type : Research Article


1 Master of Remote Sensing Engineering, Faculty of Civil Engineering, Ferdowsi University, Mashhad, Iran

2 Department of Civil Engineering, Ferdowsi University, Mashhad, Iran

3 Department of Geomatics, Khajeh Nasir Toosi University of Technology, Tehran, Iran


In this article, new index, the Optimized Synthetized Drought Index (OSDI), is suggested for real-time monitoring of this phenomenon in areas with heterogeneous land covers. The precipitation, one of the important factors in drought, measured by ground stations and improved byTRMM satellite data. Each of the three normalized moisture indices, including Normalized Difference Vegetation Index (NDVI), Visible Short-Infrared Drought Index (VSD), and the Surface Water Capacity Index (SWCI), individually enter to principal component analysis (PCA), with Precipitation Condition Index (PCI) and land surface temperature index (LST). Themaincomponentsof these PCA are definedasSynthetized Drought Indices, called SDI1, SDI2, and SDI3.Then, the performance of three output indices is evaluated by using the Standardized Precipitation Index (SPI) in 1 and 3 month scale.Validation results indicate that the synthesized index of PCA on the normalized VSDI, LST and PCI, provides the best performance in real-time monitoring of drought.This Index is called (OSDI). Then, 42 Landsat7 images were employed to evaluate the ability of suggested indices inheterogeneous land. The three normalized indices of NDVI, SWCI and VSDI, as the only effective factor in different performance of SDI1, SDI2 and OSDI, is produced by the spectral bands of Landsat7 images and their performance in various lands covers were evaluated using SPI index. High correlation between normalized-VSDI and one-month-SPIhas approved capability of OSDI in real-time monitoring of drought in heterogeneous areas. OSDI maps showed that in the provinces of Tehran and Qom of Iran, in 2008, 2009, and 2014, a severe drought has occurred in Central and South-East regions of Tehran, and also at central and northern parts of Qom.


[1]- Niazi Y, Talebi A, Mokhtari M.H, Vazifedoust M. Evaluating a new combination of drought index based on remote sensing data (RCDI) within the Central Iran. Iranian Journal of Eco Hydrology. 2017; 3(1):31-43.[Persian]
[2]-Zhang N, Hong Y, Qin Q, Liu L. VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. International journal of remote sensing. 2013; 34(13):4585-609.
[3]-Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, Huang Y. A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation. 2013;23:245-53.
[4]- McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. InProceedings of the 8th Conference on Applied Climatology 1993; 17(22): 179-183.
[5]- Jahangir M, Khoshmashraban M, Yousefi H. Monitoring and forcasting drought conditions using standardized precipitation index (SPI) and Artificial Neural Network (Case Study: Tehran and Alborz provinces). Iranian Journal of Eco Hydrology. 2016; 2(4):417-28.[Persian]
[6]- Kogan FN. Global drought watch from space. Bulletin of the American Meteorological Society. 1997;78(4):621-36.
[7]- Hao C, Zhang J, Yao F. Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation. 2015;35:270-83.
 [8]- Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment. 1979;8(2):127-50.
 [9]-Kogan FN. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. 1995;15(11):91-100.
 [10]-Kogan FN. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society. 1995;76(5):655-68.
 [11]-Huete AR. A soil-adjusted vegetation index (SAVI). Remote sensing of environment. 1988;25(3):295-309.
 [12]-Huete AR, Tucker CJ. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing. 1991;12(6):1223-42.
[13]-Ghulam A, Qin Q, Zhan Z. Designing of the perpendicular drought index. Environmental Geology. 2007;52(6):1045-52.
 [14]-Ghulam A, Qin Q, Teyip T, Li ZL. Modified perpendicular drought index (MPDI): a real-time drought monitoring method. ISPRS Journal of Photogrammetry and Remote Sensing. 2007;62(2):150-64.
[15]-Bajgiran PR, Darvishsefat AA, Khalili A, Makhdoum MF. Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments. 2008;72(6):1086-96.
[16]-Wang J, Rich PM, Price KP. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing. 2003;24(11):2345-64.
[17]-Ghulam A, Li ZL, Qin Q, Yimit H, Wang J. Estimating crop water stress with ETM+ NIR and SWIR data. Agricultural and forest meteorology. 2008;148(11):1679-95.
[18]-Shahabfar A, Ghulam A, Eitzinger J. Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation. 2012;18:119-27.
[19]-Bhuiyan C, Singh RP, Kogan FN. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 2006;8(4):289-302.
[20]-Jain SK, Keshri R, Goswami A, Sarkar A, Chaudhry A. Identification of drought‐vulnerable areas using NOAA AVHRR data. International Journal of Remote Sensing. 2009;30(10):2653-68.
[21]-Kogan F, Stark R, Gitelson A, Jargalsaikhan L, Dugrajav C, Tsooj S. Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. International Journal of Remote Sensing. 2004;25(14):2889-96.
[22]-Zhang A, Jia G. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote sensing of Environment. 2013;134:12-23.
 [23]-Rhee J, Im J, Carbone GJ. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment. 2010;114(12):2875-87.
[24]-Almazroui M. Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmospheric Research. 2011;99(3):400-14.
[25]-Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment. 2002;83(1):195-213.
[26]-Quiring SM, Ganesh S. Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agricultural and Forest Meteorology. 2010;150(3):330-9.
 [27]-Zhang HW, Chen HL, Shen S, Zhou G, Yu W. Drought remote sensing monitoring based on the surface water content index (SWCI) method. Remote Sensing Technology and Application. 2008; 23(6):624-8.
[28]-Chuvieco E, Riano D, Aguado I, Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing. 2002;23(11):2145-62.
[29]-Ceccato P, Flasse S, Tarantola S, Jacquemoud S, & Grégoire J. M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote sensing of environment. 2001; 77(1), 22-33.
[30]-Kogan FN. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing. 1990;11(8):1405-19.
[31]-Singh RP, Roy S, Kogan F. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. International Journal of Remote Sensing. 2003;24(22):4393-402.
 [32]-Hooshangi N, Alesheikh A.A, Helali H. Regional review of potential solar radiation.Evaluation and optimization of interpolation methods in the country. Journal of Regional Planning. 2014; 4: 1-16. [Persian]
Volume 3, Issue 3
September 2017
Pages 439-454
  • Receive Date: 19 September 2016
  • Revise Date: 24 November 2016
  • Accept Date: 19 November 2016
  • First Publish Date: 19 November 2016
  • Publish Date: 22 September 2016