We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This dataset was produced by the Joint Remote Sensing Research Program using data sourced from the European Space Agency (ESA) Copernicus Sentinel Progam.
This product captures surface reflectance at seasonal (ie three-monthly) time scales, forming a consistent time series from late 2015 - present. For applications that focus on vegetation changes, the fractional cover and ground cover products may be more suitable. For longer time periods, the Landsat-derived products may be more suitable.
The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.
Sentinel 2 Level 1C downloaded > Masks applied > Mediod calculated
Image Pre-processing: Sentinel-2 data was downloaded from the ESA as Level 1C (version 02.04 system). Masks for cloud, cloud shadow, topographic shadow and water were applied as described in Flood (2017). The resulting imagery is expressed as surface reï¬ectance. Cloud, cloud shadow and snow have been masked out using the Fmask automatic cloud mask algorithm. Topographic shadowing has been masked using the Shuttle Radar Topographic Mission DEM at 30 m resolution, and the methods described by Robertson (1989).
Seasonal Surface Reflectance: The 6 Landsat-like reflectance bands were stacked together, and the medoid calculated in the resulting 6-dimensional space of reï¬ectance values. The medoid is a “measure of centre” of a multi-variate set of points, similar in nature to the median of a univariate dataset. In a general cluster of points, in n-dimensional space, the medoid will lie roughly in the centre of the cluster, making it a good choice as representative of that set of points. Most importantly, it is robust against the presence of outliers in the set, until at least half of the points are to be considered as outliers, after which it breaks down. If a given pixel has less than three observations available for the season, after masking, we deï¬ne the result as missing, on the principle that we do not have enough data to know how representative our choice might be. For further details on this method see Flood (2013).