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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39 For more details click here
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(7): 1125-1136
DOI: https://doi.org/10.20546/ijcmas.2020.907.132


Drought Assessment using Standardized Precipitation Index and Normalized Difference Vegetation Index
Aishwarya Panda*, Narayan Sahoo, Balram Panigrahi and Dwarika Mohan Das
Department of Soil and Water Conservation Engineering, CAET, OUAT, Bhubaneswar, Odisha, India
*Corresponding author
Abstract:

The present study was carried out to assess the meteorological drought using Standardized Precipitation Index (SPI), agricultural drought using Normalized Difference Vegetation Index (NDVI) in Nuapada district of Odisha. SPI is a popular meteorological drought index which is designed to quantify the precipitation deficit for multiple time scales. NDVI is a vegetation index to represent agricultural drought based on remote sensing data. Comparison between SPI and NDVI was made to assess the potentiality of these indices to predict the actual drought condition a better way. The results indicated that there were mismatches between SPI and Odisha State Disaster Management Authority (OSDMA) drought information whereas the drought risk assessment based on NDVI values was much better correlated with the actually observed drought on ground. Hence, NDVI is found to be more suitable for effective agricultural drought prediction.


Keywords: NDVI, SPI, LANDSAT, GIS, OSDMA
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How to cite this article:

Aishwarya Panda, Narayan Sahoo, Balram Panigrahi and Dwarika Mohan Das. 2020. Drought Assessment using Standardized Precipitation Index and Normalized Difference Vegetation Index.Int.J.Curr.Microbiol.App.Sci. 9(7): 1125-1136. doi: https://doi.org/10.20546/ijcmas.2020.907.132