Scheduled maintenance: 8-10 am ACT/NSW each Wednesday. Expect shutdowns and restarts.

Contact us: data at  |  Disclaimer: Please read
Product pages » Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage

Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage

Last modified by Bec Trevithick on 2019/02/20 14:05

Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage


Link to the data

DescriptorData linkLayer name
Dataset digital object identifier (DOI)Not Available Yet 
GeoNetwork recordNot Available Yet

Tiff mosaics (THREDDS) 
Tiff mosaics (FTP)

Data licence and Access rights

RightsCopyright 2010-2020. JRSRP. Joint Remote Sensing Research Project (JRSRP).
LicenceCreative Common Attribution (CC-BY) 4.0
AccessWhile every care is taken to ensure the accuracy of this information, the Joint Remote Sensing Research Project makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.

Abstract or Summary

The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 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. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. Intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. Resample of the two 20m bands is using cubic convolution.

Spatial and Temporal extents

Spatial resolution (metres)10
Spatial coverage (degrees)north:-6; south:-45; west:129 east:160  
Temporal resolutionSeasonally - At least one image per standard calendar season.
Temporal coverageAutumn 2016 ongoing
Sensor & platformSentinel-2
Spatial representation typegrid
Spatial reference systemAustralian Albers. EPSG:3577

Point of contact

NameRebecca Trevithick
OrganisationQLD Department of Science, Information Technology and Innovation
PositionSenior Scientist (Remote Sensing)
AddressRemote Sensing Centre, DSITI, EcoSciences Precinct
Telephone+61 7 3170 5679


  • Joint Remote Sensing Research Program.
  • ESA Copernicus Sentinel-2


FoREnvironmental Sciences > Ecological Applications = 0501

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (
  2. Climate and Forecast (CF) convention standard names (
  3. Fields of Research codes (

Data quality

Horizontal Positional Accuracy

All the data described here has been generated from the analysis of Sentinel-2 data, which has a spatial resolution of approximately 10 m in the Blue, Green, Red and Near Infra-red (NIR) bands, and 20 m in the two Short Wave Infra-red (SWIR) band. The 20 m bands have been resampled to 10 m using cubic convolution, to provide a consistent 10 m data set. The imagery is rectified during processing by the European Space Agency (ESA), and not modified spatially beyond that.The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m, and they are in the process of improving it.

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

Vertical Positional Accuracy

This product does not include any vertical positional information.


Filenaming convention

Filenames for the seasonal fractional cover deciles product conforms to the AusCover standard naming convention. The standard form of this convention is:

<satellite category code><instrument code><product code>_<where>_<when>_<processing stage code>_<additional dataset specific tags>

Details for the unique codes used for this dataset can be found in the following table.

 Data Naming Element Possible
Standard Elements  
satellite categorycvSentinel-2 - all possible
when myyyymmyyyymm season start date (1st day of month) and season end date (last day of month) 
processing stageabmseasonal surface reflectance
data projectiona2Australian Albers Equal Area

Related products

Seasonal surface reflectance - Landsat


ItemDetail or link

Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500; doi:10.3390/rs5126481


Flood, N., Danaher, T., Gill, T. and Gillingham, S. (2013) An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sens. 2013, 5(1), 83-109. doi:10.3390/rs5010083


Robertson, P. Spatial transformations for rapid scan-line surface shadowing. IEEE Comput. Graph.Appl. 1989, 9, 30–38.

PublicationZhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. Extra test performed to mask saturated cloud.  Cloud shadow mask, from Fmask Landsat TM cloud algorithm. Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94.

Algorithm summary

All input Sentinel-2 imagery was downloaded from the European Space Agency website as level L1C imagery. The imagery has then 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 (1989).

The seasonal composites were calculated from this masked surface reflectance using the medoid. The 6 Landsat-like reflectance bands were stacked together, and the medoid calculated in the resulting 6-dimensional space of reflectance 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. 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. Please see the published paper (Flood, 2013) on this algorithm for full details.

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.

The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.

Product version history

Version labelDetail
1.0Initial release

Metadata history

2018-01-18Metadata creation date
Created by Bec Trevithick on 2018/01/16 13:45

This wiki is licensed under a Creative Commons 2.0 license
XWiki Enterprise 4.5.3 - Documentation