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Product pages » Seasonal fractional cover - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage

Seasonal fractional cover - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage

Last modified by Bec Trevithick on 2019/02/20 13:57

Seasonal fractional cover - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage


Link to the data

DescriptorData linkLayer name
Persistent URLNot Available Yet 
GeoNetwork recordNot Available Yet

TIFF mosaics - FTP access

Geoserver example,-4919280.0,2194820.0,-1138330.0&width=532&height=768&srs=EPSG:3577&format=application/openlayersaus:sentinel_fractional
Timeseries Tool http://vegmachine.netsentinel fractional cover

Data licence and Access rights

RightsCopyright 2010-2020. JRSRP. Rights owned by the 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 (JRSRP) 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.

Alternate title

Seasonal fractional vegetation cover for Australia derived from Sentinel-2 images

Abstract or Summary

Land cover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. This model was originally developed for Landsat imagery, but has been adapted for us with Sentinel-2 imagery to produce a 10 m resolution equivalent product. 

The free availability of the complete Sentinel-2 time series provides the opportunity to composite imagery into representative seasonal images. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013).

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 Progam


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 stageacaseasonal fractional cover from surface reflectance inputs
data projectiona2Australian Albers Equal Area

Related products

Landsat Fractional Cover

SLATS Star Transects


ItemDetail or link
PublicationArmston, J. D., Danaher, T.J., Goulevitch, B. M., and Byrne, M. I., (2002). Geometric correction of Landsat MSS, TM, and ETM+ imagery for mapping of woody vegetation cover and change detection in Queensland. Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference, Brisbane, Australia, September 2002.
PublicationDanaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., and Gillingham, S. (2010). Ecosystem Function in Savannas: Measurement and Modelling at Landscape to Global Scales. Vol. Section 3. Remote Sensing of Biophysical and Biochemical Characteristics in Savannas How different remote sensing technologies contribute to measurement and understanding of savannas. Taylor and Francis, Remote sensing of tree-grass systems: The Eastern Australian Woodlands.
PublicationDanaher, T. and Collett, L (2006). Development, Optimisation and Multi-temporal Application of a Simple Landsat Based Water Index. Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, Australia, November 2006.
Publicationde Vries, C., Danaher, T., Denham, R., Scarth, P. & Phinn, S. (2007). An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites, Remote Sensing of Environment, vol. 107, no. 3, pp. 414-429.

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


Flood, N. (2017) Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens. 2017, 9(7), 659. doi:10.3390/rs9070659

PublicationMuir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., Stewart, J., 2011. Guidelines for Field measurement of fractional ground cover: a technical handbook supporting the Australian collaborative land use and management program. Tech. rep., Queensland Department of Environment and Resource Management for the Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra.
PublicationRobertson, K. (1989)  Spatial transformation for rapid scan-line surface shadowing, IEEE Compter Graphics and Applications.
PublicationScarth, P., Röder, A., Schmidt, M., 2010b. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis. In: Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference (ARSPC), 13-17 September, Alice Springs, Australia. Alice Springs, NT.
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

Image Pre-Processing

All input Sentinel-2 imagery was downloaded from the European Space Agency (ESA) website as level L1C imagery. The imagery has been corrected for atmospheric effects, and bi-directional reflectance and topographic effects, using the methods detailed by Flood et al (2013). This is a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects. It was originally developed for Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery, and has been adapted for use with Sentinel-2 by the inclusion of the correct spectral response functions, as supplied by ESA. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention. 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. Water has been masked out using the methods outlined in Danaher & Collett, (2006).

Fractional Cover Model

The bare soil, green vegetation and non-green vegetation endmembers for the combination of Landsat-5 TM and Landsat-7 are calculated using models linked to an intensive field sampling program whereby more than 1500 sites covering a wide variety of vegetation, soil and climate types were sampled to measure overstorey and ground cover following the procedure outlined in Muir et al (2011). A constrained linear spectral unmixing is applied to the image archive using the derived endmembers and has an overall model Root Mean Squared Error (RMSE) of 11.6%. The model originally developed using Landsat imagery has been adapted for Sentinel-2 imagery using the reflectance adjustment factors calculated by Flood (2017), to ensure consistency of reflectance data between the Landsat and Sentinel-2 instruments. 

Values are reported as percentages of cover. The fractions stored in the 4 image layers are:  Band1 - bare (bare ground, rock, disturbed), Band2 - green vegetation, Band3 - non green vegetation (litter, dead leaf and branches), Band4 - Model fitting error.  

Seasonal Compositing

The method of compositing used in the creation of the seasonal fractional cover product is the selection of representative pixels through the determination of the medoid of three months (a season) of fractional cover imagery. The medoid is a multi-dimensional equivalent of the median. Within the three-dimensional space of the cover fractions, the medoid is the point which minimises the total distance between the selected point and all other points. Thus the selected point is “in the middle” of the set of points, in terms of the cover fractions. Like the median, the value selected is a specific data point and not an averaged or blended value made up of different image layers. The pixel selected is therefore a true representative pixel. In addition, because it selects the centrally located point in the multi-dimensional space, it is robust against extreme values, inherently avoiding the selection of outliers, such as occurs when cloud or cloud shadow goes undetected. For further details on this method see Flood (2013). The nature of the medoid means that for each pixel in the representative image created at least three pixels from the time-series of imagery for the season must be available. Unfortunately, due to the high level of cloud cover in some areas, often three cloud free pixels are not available, resulting in data gaps in the resulting seasonal fractional cover image.

Data storage
A 4 band (byte) image is produced:

  • band 1 – bare ground fraction (in percent) 
  • band 2 - green vegetation fraction (in percent) 
  • band 3 – non-green vegetation fraction (in percent) 
  • band 4 – Error Layer representing the RMSE between the predicted pixel value and the actual pixel value on a nominal scale of 0 (no error) to 100 (very large error).

Product version history

Version labelDetail
1.0Initial release

Metadata history

2018-01-20Metadata creation date
Created by Bec Trevithick on 2018/01/16 13:45

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