Comparison of SPI [Standardized Precipitation Index] and IDSI [Integrated Drought Severity Index] applicability for agriculture drought monitoring in Sri Lanka
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Pani, Peejush; Alahacoon, Niranga; Amarnath, Giriraj; Bharani, Gurminder; Mondal, S.; Jeganathan, C. 2016. Comparison of SPI [Standardized Precipitation Index] and IDSI [Integrated Drought Severity Index] applicability for agriculture drought monitoring in Sri Lanka. Paper presented at the 37th Asian Conference on Remote Sensing (ACRS): Promoting Spatial Data Infrastructure for Sustainable Economic Development, Colombo, Sri Lanka, 17-21 October 2016. 8p.
Permanent link to cite or share this item: https://hdl.handle.net/10568/82779
Increasing frequency of drought events coupled uncertainty imparted by climate change pose grave threat to agriculture and thereby overall food security. This is especially true in South Asian region where world’s largest concentration of people depends on agriculture for their livelihood. Indices derived from remote sensing datasets signifying different bio-physical aspects are increasingly used for operational drought monitoring. This study focuses on evaluating a newly created index for agricultural drought referred as Integrated Drought Severity Index (IDSI) in comparison with the traditional Standardized Precipitation Index (SPI) primarily representing precipitation condition to delineate drought using custom created ArcGIS toolbox for a period of fourteen years (2001-2014) in Sri Lanka. SPI created using remotely sensed PERSIANN precipitation dataset was compared with the IDSI created using hybrid datasets. IDSI is created based on seamless mosaic of remotely sensed multi-sensor data that takes vegetation (computed from MODIS data product MOD09A1), temperature (MOD11A2) and precipitation (TRMM & GPM) status into consideration. The comparative study was made to assess the efficiency of newly created index and ArcGIS toolbox techniques for near real-time monitoring of spatio-temporal extent of agricultural drought. The result showed significant correlation of 0.85 between the two indices signifying the potential of using IDSI that integrates the response of agriculture drought variables (vegetation, rainfall, temperature and soil moisture) in monitoring shortterm drought and application in risk reduction measures.