Water bodies, such as lakes and rivers, are a critical source of water across Africa, and their presence is becoming increasingly variable due to more frequent occurrences of droughts and floods brought on by climate change. The Digital Earth Africa program has developed a provisional continental Waterbodies service, which maps over 700,000 water bodies and tracks their surface area dynamics over the last 38 years. In this paper, we discuss the co-design and development of Digital Earth Africa’s provisional continental Waterbodies service, initial results, and next steps.

We present the latest development in the Digital Earth Africa (DE Africa) program on application development using analysis-ready data, development of tools for accessing commercial imagery, and evaluation of additional analysis-ready data sources. We discuss the significant achievements and challenges encountered and highlight future work essential for the program’s goal to empower African countries in climate actions.

Every scientific experiment starts with the data, which needs to be fine-tuned for the specific use-case. We call this "analysis ready data (ARD)". In some cases, for the sake of reusability and comparability, the specifications on how ARD should be prepared, are well defined - CEOS is working hard in this direction. In many other cases, however, the procedures are not yet mature enough to support standardisation. In Earth Observation (EO) field this is especially true, as the whole community is moving from (semi) manually analysing individual scenes, from the time there were any data barely available, to processing of time-series, now that Landsat and Sentinel made this possible. We are now even facing a problem where there is simply too much of data, with PBs of open and commercial imagery being readily available. Machine learning (ML) approach can address the challenge of shifting through data, but ML as well requires data to be pre-processed for purpose. Therefore, it is essential to have facility, which can generate ARD data customised for the specific analysis' requirements. Sentinel Hub is one of such tools.

This paper outlines a comparative study where Sentinel-1 Interferometric Wide Swath (IWS) data obtained from two different sources - the Google Earth Engine and the Sentinel Hub by Sinergise - have been compared with respect to their geometric and radiometric characteristics. Assessed over five study sites with different land cover and topographic features - in Australia, Brazil, Ethiopia, Indonesia and the USA - the results indicate comparable absolute geolocation accuracy between the two sets of data. Radiometric performance was also similar within the main (0 - -30 dB) dynamic range of the data.

Digital Earth Africa is a continental infrastructure that makes Earth observation data available to support sustainable development in Africa. Access to analysis ready data, i.e., data that are processed to a defined set of requirements and ready for immediate analysis, is key to the success and sustainability of the program. In this paper, we present our strategy around analysis ready data and outline the efforts taken to ensure operational supply of these datasets for the African continent. We also share our experience and learnings during this process.