For the first time in Africa, analysis ready Sentinel-1 Synthetic Aperture Radar (SAR) data is now routinely processed for the entire continent and made available through the DE Africa platform. The time series data is currently available at approximately 20 metre spatial resolution, every 6 to 12 days from July 2018 to the present day.
DE Africa is working with European Earth Observation (EO) industry leader Sinergise to produce this Sentinel-1 Normalized Radar Backscatter (NRB) dataset according to the Committee on Earth Observing Satellites (CEOS) Analysis Ready Data (ARD) specification. For this data, Radiometric Terrain Correction (RTC) has been applied so data acquired with different imaging geometries can be compared.
Sentinel-1 image of an area on Nigeria coast near Port Harcourt. See our gallery for more Sentinel-1 imagery from DE Africa
Countries that experience frequent clouds often find other satellite imagery less effective, but the use of radar data can overcome this challenge. SAR’s ability to ‘see through’ clouds makes this dataset critical for mapping and monitoring land cover changes in the wet tropics. The dual polarisation backscatter time series can be used in applications for forests, agriculture, wetlands and land cover classification.
To make the data easier to use, it will be processed according to Analysis Ready Data (ARD) specifications developed by the Committee on Earth Observing Satellites (CEOS). ARD reduces the barrier to use, allowing thematic specialists in areas such as land cover, forest management, agriculture and water resources to apply the data.
While satellite radar data has many uses, the applications traditionally call for high levels of expertise in pre-processing and interpretation. CEOS has been developing ARD specifications since 2016 to allow data to be processed in an easier to use and interoperable format. The Normalised Radar Backscatter is the simplest product as it allows changes through time to be detected, for example it is used in Switzerland to measure changes in seasonal snow cover.
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