Index insurance pay-out is made when a pre-determined index (e.g. soil moisture, rainfall) that can be measured remotely with EO, and other means, falls above or below a predetermined threshold. Detailed verification of losses at field level are not necessary once the index is tuned to a region and pay-out thresholds are defined. Satellite-based soil moisture has been found to be an effective index insurance parameter as it displays the actual available water on the ground needed for vegetation development.
This example highlights an index insurance service that comprises different levels of output products, depending on the user requirements and available input data. Apart from the tangible raw data products, the end users will receive different levels of index insurance products, making them more resilient to climate shocks and enabling them to fulfil the contractual agreements with banks and microfinance institutions.
- Level 1: A first indication of the climatic situation in a region can be derived from historic soil moisture trend analyses and anomaly observations. This is based on coarse resolution surface soil moisture data, indicating changes in moisture pattern. Completed by observing trends and anomalies in rainfall and vegetation parameters, a comprehensive picture of the region of interest can be drawn.
- Level 2: The next level is the derivation of the start of the wet season information based on satellite soil moisture data, both historic and near-real time.
- Level 3: This information can then be refined by employing ground information (yield data) as well as satellite based vegetation dynamics. By incorporating yield data the response of different crop types to varying levels of soil moisture availability can be observed.
Historic and near-real time global soil moisture observations (low and medium spatial resolution), global vegetation indices, global rainfall estimates (optional), crop yield data (optional). The important role of soil moisture for the environment and climate system is well known and it was recognized by GCOS as an ECV in 2010. Satellite measurements integrate this rather heterogeneous variable over relatively large areas, but temporal resolution is very high and thus ideally suited for index insurance applications.
For long-term trend analyses and anomaly detection the ESA CCI soil moisture dataset will be employed. This most complete and most consistent global soil moisture data record is based on active and passive microwave sensors providing the soil moisture content in the first 5-10cm of the soil in absolute values (m3/m3). The data is provided at a spatial resolution of 0.25 degree for the period of 1978 to 2017. By the time of the ESA EO4SD project the data series will also cover the year 2018. Via the European Centre for Medium-range Weather Forecasts (ECMWF), the C3S will continue the provision of the gridded soil moisture observations, extending the observations in the coming years (see Dorigo et al 2017, Gruber et al 2017, Liu et al 2012).
For near-real time, operational monitoring soil moisture data will be available at two resolutions.
- The Copernicus Land Service provides operational, near-real time Soil Water Index (SWI) data from 2007 at daily to 10 daily resolutions, representing the soil moisture content in the first meter of the soil in relative units [0-100%] ranging between wilting level (dry - 0%) and field capacity (wet - 100%). It is provided at approximately 0.1 degree spatial resolution for the period 2007 to present.
- A globally available, 5-daily, 10m soil moisture commercial service will be available (Dec 2018) from GeoVille stating with volumetric (m³/m³) soil moisture observations from Sentinel-1. Besides the observations also anomalies are provided.
Global vegetation indices can be derived from various sources such as Copernicus and C3S, amongst others.