Analysis of Agricultural Drought in Sone Command Area in Bihar, India using the Vegetation Condition Index
| International Journal of Civil Engineering |
| © 2026 by SSRG - IJCE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : Saurav Shekhar Kar, Anupama A. Athawale, K. Praveen, Rahul Ray |
10.14445/23488352/IJCE-V13I3P117
How to Cite?
Saurav Shekhar Kar, Anupama A. Athawale, K. Praveen, Rahul Ray, "Analysis of Agricultural Drought in Sone Command Area in Bihar, India using the Vegetation Condition Index," SSRG International Journal of Civil Engineering, vol. 13, no. 3, pp. 240-246, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I3P117
Abstract:
Agricultural drought poses a significant threat to crop productivity, rural livelihoods, and regional food security, particularly in irrigation-dependent regions where climatic variability and water management practices interact. Although canal irrigation systems are designed to buffer rainfall deficits, spatial and seasonal variability in vegetation stress often persists. This study assesses agricultural drought in the Sone Command Area, Bihar, India, using the Vegetation Condition Index (VCI) derived from Landsat-based Normalized Difference Vegetation Index (NDVI) for the years 2000, 2010, and 2020. Seasonal analysis was conducted for pre-monsoon and post-monsoon periods to evaluate intra-annual drought dynamics and long-term variability over two decades. NDVI was computed using red and near-infrared bands of Landsat imagery, and VCI was derived by normalizing NDVI values against historical minimum and maximum conditions to quantify relative vegetation stress. The results reveal pronounced seasonal contrasts, with pre-monsoon periods exhibiting significantly higher drought severity compared to post-monsoon seasons. The percentage of drought-affected area during pre-monsoon was estimated at 75.23% in 2000, 93.78% in 2010, and 19.75% in 2020, indicating extreme vegetation stress in 2000 and 2010, followed by notable improvement in 2020. In contrast, post-monsoon drought-affected areas were 47.52%, 44.98%, and 29.36% for the respective years, demonstrating relatively reduced stress conditions after monsoon rainfall. The analysis highlights pronounced seasonal contrasts and spatial heterogeneity in vegetation stress within the irrigation command. The findings demonstrate that Landsat-derived VCI provides a reliable, high-resolution tool for monitoring agricultural drought in irrigation-supported regions and offers valuable insights for drought mitigation planning and water resource management.
Keywords:
Agricultural drought, NDVI, Vegetation Condition Index, Landsat, Sone Command Area, Seasonal drought analysis.
References:
[1] Dipanwita Dutta et al., “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, vol. 18, no. 1, pp. 53-63, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Sanjay K. Jain et al., “Application of Meteorological and Vegetation Indices for Evaluation of Drought Impact: A Case Study for Rajasthan, India,” Natural Hazards, vol. 54, no. 3, pp. 643-656, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[3] N.R. Patel et al., “Analysis of Agricultural Drought using Vegetation Temperature Condition Index (VTCI) from Terra/MODIS Satellite Data,” Environmental Monitoring and Assessment, vol. 184, no. 12, pp. 7153-7163, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Dipanwita Dutta, Arnab Kundu, and N.R. Patel, “Predicting Agricultural Drought in Eastern Rajasthan of India using NDVI and Standardized Precipitation Index,” Geocarto International, vol. 28, no. 3, pp. 192-209, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Donald A. Wilhite, Drought as a Natural Hazard, Concepts and Definitions, 1st ed., Routledge, 2016.
[Google Scholar] [Publisher Link]
[6] Ashok K. Mishra, and Vijay P. Singh, “A Review of Drought Concepts,” Journal of Hydrology, vol. 391, no. 1-2, pp. 202-216, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wenzhe Jiao et al., “Evaluating an Enhanced Vegetation Condition Index (VCI) based on VIUPD for Drought Monitoring in the Continental United States,” Remote Sensing, vol. 8, no. 3, pp. 1-21, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R.A. Seiler, F. Kogan, and Guo Wei, “Monitoring Weather Impact and Crop Yield from NOAA AVHRR Data in Argentina,” Advances in Space Research, vol. 26, no. 7, pp. 1177-1185, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jue Wang, K.P. Price, and P.M. Rich, “Spatial Patterns of NDVI in Response to Precipitation and Temperature in the Central Great Plains,” International Journal of Remote Sensing, vol. 22, no. 18, pp. 3827-3844, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Lei Ji, and Albert J. Peters, “Assessing Vegetation Response to Drought in the Northern Great Plains using Vegetation and Drought Indices,” Remote Sensing of Environment, vol. 87, no. 1, pp. 85-98, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Saptarshi Mondal et al., “Extracting Seasonal Cropping Patterns using Multi-Temporal Vegetation Indices from IRS LISS-III Data in Muzaffarpur District of Bihar, India,” The Egyptian Journal of Remote Sensing and Space Science, vol. 17, no. 2, pp. 123-134, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Susan Barati et al., “Comparison the Accuracies of Different Spectral Indices for Estimation of Vegetation Cover Fraction in Sparse Vegetated Areas,” The Egyptian Journal of Remote Sensing and Space Science, vol. 14, no. 1, pp. 49-56, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Dipanwita Dutta, N.R. Patel, and Arnab Kundu, “Analyzing the Performance of Auto Regressive Integrated Moving Average (ARIMA) Model for Predicting Agricultural Productivity in Eastern Rajasthan,” Research Journal of Agricultural Sciences, vol. 2, no. 3, pp. 555-559, 2011.
[Google Scholar]
[14] Steven M. Quiring, and Srinivasan Ganesh, “Evaluating the Utility of the Vegetation Condition Index (VCI) for Monitoring Meteorological Drought in Texas,” Agricultural and Forest Meteorology, vol. 150, no. 3, pp. 330-339, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[15] C. Domenikiotis et al., “Early Cotton Yield Assessment by the Use of the NOAA/AVHRR Derived Vegetation Condition Index (VCI) in Greece,” International Journal of Remote Sensing, vol. 25, no. 14, pp. 2807-2819, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Nguyen Thanh Son et al., “Monitoring Agricultural Drought in the Lower Mekong Basin using MODIS NDVI and Land Surface Temperature Data,” International Journal of Applied Earth Observation and Geoinformation, vol. 18, pp. 417-427, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ramesh P. Singh, Sudipa Roy, and F. Kogan, “Vegetation and Temperature Condition Indices from NOAA AVHRR Data for Drought Monitoring Over India,” International Journal of Remote Sensing, vol. 24, no. 22, pp. 4393-4402, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[18] F. Kogan, and J. Sullivan, “Development of Global Drought-Watch System using NOAA/AVHRR Data,” Advances in Space Research, vol. 13, no. 5, pp. 219-222, 1993.
[CrossRef] [Google Scholar] [Publisher Link]