Publications

Sruthi, S.; Aslam, M. A. Mohammed (2015). Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE'15), 4, 1258-1264.

Abstract
Increasing temperature and altered precipitation patterns, leads to the extreme weather events like Drought which drastically affects the agricultural production. Agricultural drought is nothing but the decline in the productivity of crops due to irregularities in the rainfall as well as decrease in the soil moisture, which in turn affects the economy of the nation. As the Indian agriculture is largely dependent on the Monsoon, a slight change in it affects the production as well as the crop yield drastically. The agricultural drought monitoring, assessment as well as management can be done more accurately with the help of geospatial techniques like Remote Sensing.. Raichur District, of Karnataka (India) falls in a plateau region and located between 15 degrees 33' and 16 degrees 34' N latitudes and 76 degrees 14' and 77 degrees 36' E longitudes. It is a drought prone region and falls within the most arid band of the country. The district relies on the traditional agricultural based economy; hence the impact of drought on the agriculture not only affects the production but also the livelihood of common man. The purpose of the study is to analyze the vegetation stress in the Raichur district with the calculation of NDVI values and the land surface temperature (LST). The MODIS data is used for the calculation of NDVI as well as Land surface temperature. The Combination of (NDVI) normalized difference vegetation index and LST, provides very useful information for agricultural drought monitoring and early warning system for the farmers. By calculating the correlation between LST and NDVI, it can be clearly noticed that they show a high negative correlation. The correlation between LST and NDVI is -0.635 for the year 2002 and -0.586 for the year 2012. The LST when correlated with the vegetation index it can be used to detect the agricultural drought of a region, as demonstrated in this work. (C) 2015 The Authors. Published by Elsevier B.V.

DOI:
10.1016/j.aqpro.2015.02.164

ISSN:
2214-241X