Satellite-based data for agricultural index insurance: a systematic quantitative literature review

Index-based insurance (IBI) is an effective tool for managing climate risk and promoting sustainable development. It provides payouts based on a measurable index. Remote sensing data obtained from satellites, planes, UAVs, or drones can be used to design index-based insurance products. However, the extent to which satellite-based data has been used for different crop types and geographical regions has not been systematically explored. To bridge this gap, a systematic quantitative literature review was conducted to examine the use of satellite-based datasets in designing index-based insurance products. The review analyzed 89 global studies on four major types of crops: cereals, pastures and forages, perennial crops, and others (i.e., vegetables, oilseed crops, fruits, nuts, etc.). The analysis revealed a rising interest of developing index-based insurance solutions utilizing satellite-based data, particularly after 2015.
Datasets from land surface Earth observation satellites were utilized in 91% of studies with satellite-based data, outnumbering those from weather satellites. The Normalized Difference Vegetation Index (NDVI) was the most prominent satellite-retrieved vegetation index, featured in 61.2% of studies utilizing satellite imagery, revealing its effectiveness at designing and developing IBI for various crops. It has also been found that satellite-based vegetation health indices outperform weather indices and reduce basis risk with higher-spatial-resolution data. Most studies have focused on cereal crops, with fewer studies focusing on perennial crops. Countries in Asia and Africa were the most interested regions. However, research has focused on specific countries and has not been adequately spread across different regions, especially developing countries. The review suggests that satellite-based datasets will become increasingly important in designing crop-index-based insurance products. This is due to their potential to reduce basis risk by providing high resolution with adequately long and consistent datasets for data-sparse environments. The review recommends using high-spatial- and high-temporal-resolution satellite datasets to further assess their capability to reduce basis risk.