Geoscience Australia Sentinel-2B Observation Attributes Collection 3 - DEA Surface Reflectance OA (Sentinel-2B MSI)

Background: This is a sub-product of eoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 - DEA Surface Reflectance (Sentinel-2B MSI). See the parent product for more information. The contextual information related to a dataset is just as valuable as the data itself. This information, also known as data provenance or data lineage, includes details such as the data’s origins, derivations, methodology and processes. It allows the data to be replicated and increases the reliability of derivative applications. Data that is well-labelled and rich in spectral, spatial and temporal attribution can allow users to investigate patterns through space and time. Users are able to gain a deeper understanding of the data environment, which could potentially pave the way for future forecasting and early warning systems. The surface reflectance data produced by NBART requires accurate and reliable data provenance. Attribution labels, such as the location of cloud and cloud shadow pixels, can be used to mask out these particular features from the surface reflectance analysis, or used as training data for machine learning algorithms. Additionally, the capacity to automatically exclude or include pre-identified pixels could assist with emerging multi-temporal and machine learning analysis techniques. What this product offers: This product contains a range of pixel-level observation attributes (OA) derived from satellite observation, providing rich data provenance: - null pixels - clear pixels - cloud pixels - cloud shadow pixels - snow pixels - water pixels - spectrally contiguous pixels - terrain shaded pixels It also features the following pixel-level information pertaining to satellite, solar and sensing geometries: - solar zenith - solar azimuth - satellite view - incident angle - exiting angle - azimuthal incident - azimuthal exiting - relative azimuth - timedelta