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TERN/5a31eed4-e43a-404d-b534-3f820305ed61 22021

Seasonal surface reflectance - Landsat, JRSRP algorithm, Australia coverage


The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+/OLI imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values.

This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey.

This product captures surface reflectance at seasonal (ie three-monthly) time scales, forming a consistent time series from 1987 - present. For applications that focus on vegetation changes, the fractional cover and ground cover products may be more suitable.


Summary of processing: Landsat imagery less than 80% cloud cover downloaded > corrections and masking > medoid calculated for seasonal surface reflectance.
Further details are provided in the Methods section.

Temporal coverage

From 1987-12-01


  • Date modified


Citation information

How to cite this collection:

Department of Environment and Science, Queensland Government (2014): Seasonal surface reflectance - Landsat, JRSRP algorithm, Australia coverage. Version 1.0.0. Terrestrial Ecosystem Research Network (TERN). (Dataset).

Access data

This data can be accessed from the following websites

Access metadata

Source Metadata URL

Rights and Licensing

Creative Commons Attribution 4.0 International Licence

TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at

It is not recommended that these data sets be used at scales more detailed than 1:100,000.

  • Spatial coverage Hide

  • Additional information Show


      Research Groups

      Distributed by


      Image Pre-Processing
      All input Landsat TM/ETM+/OLI imagery was downloaded from the USGS EarthExplorer website as level L1T imagery. Images which the EarthExplorer site rated as having greater than 80% cloud cover were not downloaded, on the assumption that they would contribute little, and would add extra noise in the form of undetected cloud, shadow, and mis-registration (which is a greater risk in very cloudy images). The imagery has been corrected for atmospheric effects, and bi-directional and topographic effects, using the methods detailed by Flood et al. (2013). The resulting imagery is expressed as surface reflectance. 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.
      Seasonal Compositing
      The seasonal composites were calculated using the medoid in the 6-dimensional space of reflectance values from the six Landsat reflective bands. The medoid is a “measure of centre” of a multi-variate set of points, similar in nature to the median of a univariate dataset. The medoid has similar desirable properties to the univariate median. 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.
      Two variations of seasonal surface reflectance are available and can be distinguished by the ‘satellite category code’ component of the filename. (Refer to file naming convention information). ‘lz’ indicates that the composite was derived from all possible Landsat data, and ‘l8’ indicates that the composite was derived from Landsat8 OLI data only. For the latter case, the OLI Band 1 is omitted so that the resulting composite has the same bands as the generic Landsat ‘lz’ variant.
      Image Restrictions
      In estimating a value which is representative of the season, we choose to add a restriction on the number of input points. If a given pixel has less than three observations available for the season, after masking, we define the result as missing, on the principle that we do not have enough data to know how representative our choice might be. Neither the medoid nor the geometric median are robust against a single outlier in the case of less than three observations.

      Data Quality

      • The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points.
        The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail.


    • Keywords Show


      • at-surface reflectance (Unitless)


      • LANDSAT-5
      • LANDSAT-7
      • LANDSAT-8


      • TM
      • ETM+
      • OLI

      Temporal Resolution

      • Weekly - < Monthly

      GCMD Science


      ANZSRC - FOR


      User Defined

      • environment
      • imageryBaseMapsEarthCover
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