DEA Surface Reflectance OA (Landsat 8 OLI-TIRS) =============================================== Geoscience Australia Landsat 8 OLI-TIRS Observation Attributes Collection 3 ---------------------------------------------------------------------------- **Authored on**: 2018-03 **Updated on**: 2022-04 **Author/s**: Fuqin Li, David Jupp, Lan-Wei Wang, Passang Dorj, Alex Vincent, Imam Alam, Jeremy Hooke, Simon Oliver, Medhavy Thankappan, Josh Sixsmith **License**: CC BY Attribution 4.0 International License View the [original metadata page](https://cmi.ga.gov.au/data-products/dea/404/dea-surface-reflectance-oa-landsat-8-oli-tirs) for the most up-to-date information on this product. Abstract -------- - - - - - - *This is a sub-product of [**DEA Surface Reflectance (Landsat 8 OLI-TIRS)**](https://cmi.ga.gov.au/data-products/dea/365/dea-surface-reflectance-landsat-8-oli-tirs). 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 NBAR and 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 Accuracy and limitations ------------------------ #### Accuracy For information on the accuracy of the algorithms for test locations, see Zhu and Woodcock (2012) and Zhu, Wang and Woodcock (2015). #### Limitations ##### ***Fmask*** Fmask has limitations due to the complex nature of detecting natural phenomena, such as cloud. For example, bright targets, such as beaches, buildings and salt lakes often get mistaken for clouds. Fmask is designed to be used as an immediate/rapid source of information screening. The idea is that over a temporal period enough observations will be made to form a temporal likelihood. For example, if a feature is consistently being masked as cloud, it is highly probable that it is not cloud. As such, derivative processes can be created to form an information layer containing feature probabilities. Edges and fringes of clouds tend to be more opaque and can be missed by the cloud detection algorithm. In this instance, applying a morphological dilation will grow the original cloud object and capture edges and fringes of clouds. However, it is important to note that other cloud objects could also be dilated. Be mindful of single-pixel objects that could grow to become large objects. Consider filtering out these small objects prior to analysis. ##### ***Angular measurement and shadow classification*** The Digital Elevation Model (DEM) is used for identifying terrain shadow, as well as producing incident and exiting angles. It is derived from the Shuttle Radar Topography Mission (SRTM) and produced with approximately 30 m resolution. As such, any angular measurements and shadow classifications are limited to the precision of the DEM itself. The DEM is known to be noisy across various locations, so to reduce any potential extrema, a Gaussian smooth is applied prior to analysis. Contacts -------- For questions or more information about this product, email [DEA Support](mailto:dea@ga.gov.au?subject=Data%20Products%20support%20for%20DEA%20Surface%20Reflectance%20OA%20%28Landsat%208%20OLI-TIRS%29&cc=David.Jupp@csiro.au,fuqin.li@ga.gov.au,Joshua.Sixsmith@ga.gov.au).