Environmental Monitoring and Assessment

, Volume 184, Issue 12, pp 7153–7163 | Cite as

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

  • N. R. PatelEmail author
  • B. R. Parida
  • V. Venus
  • S. K. Saha
  • V. K. Dadhwal


The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.


Remote sensing MODIS Ts-NDVI space VTCI Drought Crop yields 



Authors would like to thank the MODIS Land Discipline Group for creating and sharing the MODIS LAND data. We also express our sincere thanks to Department of Agrometeorology, AAU, Anand (India) and Department of Agriculture, Gandhinagar for sharing meteorological and crop yield statistics.


  1. Boegh, E., Soegaard, H., Hanan, N., Kabat, P., & Lesch, L. (1998). A remote sensing study of the NDVI-Ts relationship and the transpiration from sparse vegetation in the Sahel based on high-resolution satellite data. Remote Sensing of Environment, 69, 224–240.CrossRefGoogle Scholar
  2. Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer soil water content and fractional vegetation cover. Remote Sensing Reviews, 9, 16–173.CrossRefGoogle Scholar
  3. Carlson, T. N., Gillies, R. R., & Schmugge, T. J. (1995). An interpretation of methodologies for indirect measurement of soil water content. Agricultural and Forest Meteorology, 77, 191–205.CrossRefGoogle Scholar
  4. Gillies, R. R., Carlson, T. N., Cui, J., Kustas, W. P., & Humes, K. S. (1997). A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurement of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature. International Journal of Remote Sensing, 18, 3145–3166.CrossRefGoogle Scholar
  5. Goetz, S. J. (1997). Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, 18, 71–94.CrossRefGoogle Scholar
  6. Jackson, R. D., Idso, S. B., Beginato, R. J., & Pinter, P. J., Jr. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138.CrossRefGoogle Scholar
  7. Larson, D. W., Jones, E., Pannu, R. S., & Sheokand, R. S. (2004). Instability in Indian agriculture-a challenge to the Green Revolution technology. Food Policy, 29, 257–273.CrossRefGoogle Scholar
  8. Moran, M. S., Clarke, T. R., Inoue, U., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246–263.CrossRefGoogle Scholar
  9. Nemani, R. R., Pierce, L. L., Running, S. W., & Goward, S. N. (1993). Developing satellite derived estimates of surface moisture status. Journal of Applied Meteorology, 28, 276–284.CrossRefGoogle Scholar
  10. Palmer, W. C. (1968). Keeping track of crop moisture conditions, nationwide: The new Crop Moisture Index. Weatherwise, 21, 156–161.CrossRefGoogle Scholar
  11. Park, S., Feddema, J. J., & Egberts, S. L. (2004). MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains. International Journal of Remote Sensing, 26, 1127–1144.CrossRefGoogle Scholar
  12. Sandholt, I., Rasmussen, K., & Anderson, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status. Remote Sensing of Environment, 79, 213–224.CrossRefGoogle Scholar
  13. Wan, Z., Wang, P., & Li, X. (2004). Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25, 61–72.CrossRefGoogle Scholar
  14. Wang, C., Qi, S., Niu, Z., & Wang, J. (2004). Evaluating soil moisture status in China using the temperature-vegetation dryness index (TVDI). Canadian Journal of Remote Sensing, 30, 671–679.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • N. R. Patel
    • 1
    Email author
  • B. R. Parida
    • 2
  • V. Venus
    • 3
  • S. K. Saha
    • 1
  • V. K. Dadhwal
    • 4
  1. 1.Indian Institute of Remote Sensing (ISRO)DehradunIndia
  2. 2.Max-Planck Institute of MeteorologyHamburgGermany
  3. 3.Department of Natural ResourcesInternational Institute for Geo-Information Science and Earth Observation (ITC)EnschedeThe Netherlands
  4. 4.National Remote Sensing Centre (ISRO)Balanagar, HyderabadIndia

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