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 (e-mail:email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
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.