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Articles | Volume XLII-3/W6
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 453–459, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-453-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 453–459, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W6-453-2019

  26 Jul 2019

26 Jul 2019

TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA

R. Das1, P. K. Das2, S. Bandyopadhyay2, and U. Raj3 R. Das et al.
  • 1TERI School of Advanced Studies, New Delhi, India
  • 2Regional Remote Sensing Centre-East, NRSC, ISRO, Kolkata, West Bengal, India
  • 3Regional Centres, NRSC, ISRO, Hyderabad, Telangana, India

Keywords: Meteorological Drought, Agricultural Drought, Trends, Vulnerability, Standardized Vegetation Index, Standardized Precipitation Index

Abstract. The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982–2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5 km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8 km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.