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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #347531

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Exploiting the convergence of evidence in satellite data for advanced weather index insurance design

item ENENKEL, M. - Columbia University
item Anderson, Martha
item OSGOOD, D. - Columbia University
item POWELL, B. - Columbia University
item BROWN, M. - University Of Maryland
item MCCARTY, J. - University Of Miami
item NEIGH, C. - Goddard Space Flight Center
item CARROLL, M. - Goddard Space Flight Center
item HAIN, C. - Goddard Space Flight Center
item HUSAK, G. - University Of California
item WOOTEN, M. - Columbia University

Submitted to: Weather, Climate, and Society
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2018
Publication Date: 1/10/2019
Citation: Enenkel, M., Anderson, M.C., Osgood, D., Powell, B., Brown, M., McCarty, J., Neigh, C., Carroll, M., Hain, C., Husak, G., Wooten, M. 2019. Exploiting the convergence of evidence in satellite data for advanced weather index insurance design. Weather, Climate, and Society. 11:65-93.

Interpretive Summary: Smallholder farmers in many parts of the developing world are particularly sensitive to variable and changing climate, given that there are often limited savings for weathering poor production years that have limited or excessive rainfall. One approach for reducing risk in these communities is to offer weather-based index insurance (WII) programs, where payouts are tied regionally to a specified index – such as rainfall – and predefined index thresholds that separate good production years from bad years with acceptable accuracy. In the past, WII has typically used gauge-based rainfall measurements or a satellite-derived vegetation index as the primary index for determining payouts. However, gauges provide incomplete spatial sampling and data may be of inconsistent quality across the insured region. Furthermore, these indices may miss other climate drivers of poor production, such as high temperatures or rapid evaporative depletion of soil moisture reserves (e.g., “flash droughts”). This study tests the added utility of incorporating new satellite-based indicators describing rainfall, soil moisture, and evaporative stress as potential information for incorporation into WII programs in Africa. The study shows that these new indicators provide useful information for detecting yield-impacting drought events, and in combination agree well with “hit rates” for severe droughts reported by farmers in Ethiopia, Senegal, and Zambia. The indicators agreed less well in their detection of moderate droughts. Next steps include development of composite indices, integrating spatiotemporal information about vegetation greenness, rainfall, soil moisture and crop evaporative stress that might better inform WII programs.

Technical Abstract: The goal of Weather Index Insurance (WII) is to protect low-income smallholder farmers against the risk of weather shocks, such as droughts, and to increase their agricultural productivity in rain-fed agricultural regions. Most operational WII approaches that use satellite data rely on rainfall and vegetation greenness as a proxy for vegetation health. However, ignoring additional moisture- and energy-related processes that influence the response of vegetation to rainfall leads to an incomplete representation of the hydrologic cycle and potentially higher basis risk. As a consequence, this study evaluates the added-value of considering multiple, independent satellite-based estimations to design, calibrate and validate weather insurance indices on the African continent. The satellite data, which are based on different physical retrieval mechanisms and algorithmic approaches, include two rainfall datasets, soil moisture, the Evaporative Stress Index (ESI), and vegetation greenness. All spatial and temporal analyses are carried out for pixels that provide information for all datasets. We limit artificial advantages by resampling all datasets to the same spatial (0.25°) and temporal (monthly) resolution. A higher correlation coefficient between the moisture-focused variables and the NDVI (Normalized Difference Vegetation Index), an indicator for vegetation vigor, provides evidence for the datasets’ capability to capture drought conditions on the ground. Our findings demonstrate that the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall dataset, satellite soil moisture and the ESI show higher correlations with the (lagged) NDVI in large parts of Africa, over different land covers and in different climate zones than the ARC2 (Africa Rainfall Climatology 2) rainfall dataset, which is often used in WII. A comparison to drought years as reported by farmers in Ethiopia, Senegal and Zambia via participative processes indicates a high “hit-rate” of all satellite-derived anomalies regarding the detection of severe droughts. However, moderate droughts are not consistently detected by all satellite-derived anomalies.