Uniting remote sensing, crop modelling and economics for agricultural risk management

The increasing availability of satellite data at higher spatial, temporal and spectral resolutions is enabling new applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of their influence on the value of risk-reduction programmes. In this Review, we discuss how approaches to estimate agricultural losses for index insurance have evolved from costly field-sampling-based campaigns towards lower-cost techniques using weather and satellite data. We identify advances in remote sensing and crop modelling for assessing agricultural conditions, but reliably and cheaply assessing production losses remains challenging in complex landscapes. We illustrate how an economic framework can be used to gauge and enhance the value of insurance based on earth-observation data, emphasizing that even as yield-estimation techniques improve, the value of an index insurance contract for the insured depends largely on how well it captures the losses when people suffer most. Strategically improving the collection and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable misestimation complement efforts to detect and protect against large losses. Improvements in earth observation are enabling new approaches to assess agricultural losses, such as those resulting from adverse weather. This Review examines advances in the application of remotely sensed data and crop modelling in index-based insurance as well as opportunities to enhance the quality of index insurance programmes. Key pointsIn many developing regions, adverse weather can lead to food insecurity, reduced investments or distressed asset sales that ensnare people in a cycle of poverty.Tools to manage risk - such as well-designed insurance - can help people avoid the most severe possible consequences of bad weather and build confidence to invest in additional income-generating opportunities.In recent decades, governments and researchers across the globe have trialled approaches to inexpensively assess agricultural losses. Index-based insurance offers promise, but detecting losses cheaply and accurately remains challenging.Recent advances in crop modelling and remote sensing can improve index-based approaches by strengthening the link between indices and actual losses, as well as reducing programme costs.We provide an economic framework to evaluate indices, suggesting how the remote sensing and modelling communities can contribute to enhancing index insurance quality through better detection of adverse conditions.Promising opportunities to enhance index insurance programmes include inexpensively addressing heterogeneous conditions on the ground, such as employing audits, optimizing insurance zones, using new sensors or increasing contract flexibility.