Satellite Based Composite Weather Insurance Product for assessing mid-term adversary

Abstract

The present study formulated a satellite-based rainfall insurance product (SRIP) at sub-seasonal scale during 2015–2020 by integrating meteorological, geophysical and vegetation index products e.g. rainfall (CHIRPS:Climate Hazards Group InfraRed Precipitation with Station), soil moisture (SMAP), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) from moderate resolution imaging spectro-radiometer (MODIS). The SRIP was meticulously designed to integrate both cause (rainfall) and impact (NDVI, NDWI, soil moisture) parameters to enhance the assessment of min-season crop risks, with a particular emphasis on deficit rainfall. The performance evaluation of SRIP was conducted in two distinct agro-climatic zones viz. Deoria district of Uttar Pradesh (Upper Gangetic Plain Region) and Krishna district of Andhra Pradesh (Southern Plateau and Hills Region) spanned from June to October, which aligns with the major crop-growing season corresponding to the Southwest monsoon period in India. Three sets of SRIPs were investigated i.e. SRIP with cause parameter (SRIP1), SRIP with cause-impact parameters with no lag response (SRIP2) and SRIP with cause-impact parameters with one-month lag response (SRIP3). The SRIP was structured to provide a numerical index on a scale ranging from 0 to 1, representing poor to good crop-growing conditions that could effectively identify the rainfall deficit threshold (SRIP≤0.43) corresponding to negative percent deviation in rainfall (PDR≤−20%). This threshold level was recognized for compensation of triggers at the multiple crop growth phases during the crop-growing season. The district average of SRIP and PDR showed fair correlation co-efficient (0.77 ≤ r ≤ 0.88). Good correlation between sub-seasonal (monthly, bi-monthly, tri-monthly) SRIP and normalized MODIS Gross Primary Productivity (0.65 ≤ r ≤ 0.98), and SRIP deviation and yield deviation (0.45 ≤ r ≤ 0.97) reflect sensitivity of SRIP to crop carbon productivity and crop yield. The findings of the present study recommend SRIP not only a multi-phase composite index for assessing mid-season crop risks in terms of rainfall deficit for clusters of insurance units, but it has the potential to be used by insurance agencies to settle the claims by farmers in a transparent and faster manner for crop loss due to large-scale rainfall deficits. However, SRIP needs to be customized in future to make it more adaptable to settle insurance claims due to weather adversaries at individual farm level.