The Copernicus Program is an ambitious initiative headed by the European Commission in partnership with the European Space Agency (ESA). The Sentinels are a constellation of satellites developed by ESA to operationalize the Copernicus program, which include all-weather radar images from Sentinel-1A and 1B, high-resolution optical images from Sentinel-2A and 2B, ocean and land data suitable for environmental and climate monitoring from Sentinel-3, as well as air quality data from Sentinel-5P.
The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument. SAR instruments are capable of acquiring meaningful data in all weather conditions (even clouds) during daytime and nighttime. Sentinel-1 data is used across many domains, including maritime activity, sea-ice mapping, humanitarian aid, crisis response, and forest management.
The Sentinel-2 mission collects high-resolution multispectral imagery useful for a broad range of applications, including monitoring of vegetation, soil and water cover, land cover change, as well as humanitarian and disaster risk.
The Sentinel-3 instrument provides systematic measurements of the planet's oceans, land, ice, and atmosphere, including the temperature, color and height of the sea surface as well as the thickness of sea ice.
The Sentinel-5 Precursor mission collects data useful for assessing air quality, including concentrations of: ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen oxide, and sulphur dioxide.
Dynamic World is a 10 m near real-time land use/land cover dataset that includes class probabilities and label information for nine classes. Predictions are generated for all historical and incoming Sentinel-2 L1C images that meet quality thresholds.
The European Space Agency WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product includes eleven land cover classes.
The S2 Cloud Probability dataset provides a flexible method to mask cloudy pixels in Sentinel-2 L1C (TOA) and L2A (SR) imagery. Per-pixel cloud probability is determined for each Sentinel-2 image in the archive at 10 m scale using the s2cloudless algorithm.
A 10 m global industrial and smallholder oil palm map for 2019. It covers areas where oil palm plantations were detected. The classified images are the output of a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites.