Do high-resolution satellite indices at field level reduce basis risk of satellite-based weather index insurance?

Abstract

PurposeSatellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.Design/methodology/approachIn this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.FindingsThe results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.Originality/valueTo the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

The role of crop classification in detecting wheat yield variation for index-based agricultural insurance in arid and semiarid environments

“…Moreover, it is clear that the increase in correlation between the index and crop yields leads to a reduction in design and basis risk (Breustedt et al, 2008;Kölle et al, 2021;Norton et al, 2012), which are crucial during the index insurance design and implementation phases (Norton et al, 2015). According to the study conducted by Pietola et al (2011), the critical threshold of index insurance from the demand side is situated at a correlation coefficient of 0.5-0.6.…”

“…One of the key requirements for applying satellite-based products for the development of agricultural index insurance is that the index should be highly correlated with crop yields (Barnett and Mahul, 2007;Siebert, 2016) in order to increase the hedging efficiency of the resulting insurance product (Breustedt et al, 2008;Kölle et al, 2021;Norton et al, 2012). In order to check the applicability and potential of the selected vegetation indices, correlation and regression analyses between wheat yield data and satellite-based indices are performed.…”

Digital opportunities for the distribution of index‐based microinsurance: Evidence from a discrete choice experiment in Mali

“…In this study, we assess a multi-peril crop insurance based on a fictional 'greenness' index inspired by the normalised difference vegetation index (NDVI). Although Turvey and McLaurin (2012) advised against purely NDVI based crop insurance products due to high basis risk, Kölle et al (2022) found that complementing weather indices with NDVI data is a viable option to reduce basis risk. This study provides the first evidence of how an insurance scheme based on satellite pictures would be perceived in Mali.…”

Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

“…To reduce basis risk, studies have explored different indices that might be well correlated with crop yields and crop losses. They have sought to design these indices by using high spatial resolution data [22][23][24], phenological information [19,25], spatial interpolation [21], spatial and temporal aggregation [26], agro-ecological information [27], and complementary satellite datasets [28,29]. In Ethiopia, Hochrainer et al Ref [30] reported that the Vegetation Health Index (VHI), measured in the late crop growth stages, explained 60% of crop yield variation.…”