Using the new H SAF 6.25km ASCAT soil moisture data for drought monitoring
Start Date:
April 16, 2025
Description:
Droughts are among the most destructive climate-related hazards, yet they often receive less immediate attention compared to rapid-onset disasters like floods. Their gradual development allows harmful conditions to build up before the full extent of the crisis is recognized. In Africa, severe droughts are frequent, leading to widespread food insecurity, livestock losses, and the displacement of a large number of people. This highlights the urgent need for more efficient, timely, and detailed drought detection and response strategies. Traditionally, drought monitoring in Africa has relied on satellite-based precipitation and vegetation indices. However, with the growing availability and refinement of satellite-derived soil moisture data, drought monitoring has the potential for significant improvement. This is because soil moisture is a more direct measure of agricultural stress than precipitation or vegetation, reflecting both rainfall and land surface temperature anomalies.
The ASCAT soil moisture service, offered by EUMETSAT, is the longest-standing operational satellite-based soil moisture services. ASCAT, which stands for Advanced Scatterometer, is an active microwave sensor onboard a series of three METOP satellites. As one of the primary microwave sensors for global soil moisture retrieval, ASCAT has been instrumental in monitoring soil moisture on a worldwide scale. The near-real-time ASCAT soil moisture service (<3h), launched in 2008, was the first of its kind and was later incorporated into the Satellite Application Facility for Support to Operational Hydrology and Water Management (H SAF). Recently, the ASCAT soil moisture retrieval algorithm was updated, marking the biggest product improvements since the inception of the service: (i) enhanced spatial sampling (6.25 km instead of 12.5 km), (ii) improved anomaly detection thanks to algorithms that account for land cover changes, (iii) better modelling of seasonal vegetation effects, and (iv) a new data format that facilitates easier comparison of near-real-time data with historical time series. This webinar will present the key changes and explain how they contribute to improved drought monitoring.
