General Information
    • ISSN: 2010-0264
    • Frequency: Bimonthly (2010-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJESD
    • Editor-in-Chief: Prof. Richard Haynes
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS), CABI, Ulrich Periodicals Directory, Electronic Journals Library, Crossref, ProQuest.
    • E-mail:
  • Jun 22, 2018 News! [CFP] 2018 the annual meeting of IJESD Editorial Board, ACESD 2018, will be held in Singapore during November 2-4, 2018.    [Click]
  • Jan 15, 2019 News! Vol.10, No.2 has been published with online version!   [Click]
The University of Queensland, Australia
It is my honor to be the editor-in-chief of IJESD. The journal publishes good papers in the field of environmental science and development.
IJESD 2015 Vol.6(5): 326-331 ISSN: 2010-0264
DOI: 10.7763/IJESD.2015.V6.612

Agricultural Drought Modeling Using Remote Sensing

B. M. Dodamani, Anoop R., and D. R. Mahajan
Abstract—Maharashtra is one of the worst drought affected states in India, and as a result the agricultural productions has kept low. In the present study Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) obtained from MODIS data (MOD13Q1) and sunspot number which represents the solar activity has been considered for drought modeling in that part of Krishna Basin which lies in Maharashtra. Two multiple linear regression models for predicting agricultural drought are developed for Kharif season, based on the data for a period of 13 years from 2000 to 2012. In the first model, previous NDVI and SPI values and in the second model Sunspot data along with NDVI and SPI values are used as variables. It was found that the coefficient of determination of the second model has improved over the first model, which suggests the high significance of solar activity in the occurrence of drought. The correlation between NDVI and SPI has been utilized in the study. Highly significant correlations were obtained between current NDVI and SPI of various time lags in the assured rainfall zone and the scarcity Zone. Crop yield model was developed from the predicted NDVI for the major crops and was validated with the actual yield. It was found that the predicted NDVI of both the fortnights of July is highly correlated with the yield of major crops in assured rainfall zone and scarcity zone.

Index Terms—Agricultural drought, crop yield prediction model, MODIS NDVI, sunspot numbers.

The authors are with National Institute of Technology Karnataka, India (,,,


Cite:B. M. Dodamani, Anoop R., and D. R. Mahajan, "Agricultural Drought Modeling Using Remote Sensing," International Journal of Environmental Science and Development vol. 6, no. 5, pp. 326-331, 2015.

Copyright © 2008-2018. International Journal of Environmental Science and Development. All rights reserved.