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Product pages » Fractional cover - MODIS, Monthly median composites, Australia coverage

Fractional cover - MODIS, Monthly median composites, Australia coverage

Last modified by Tony Gill on 2015/11/23 11:42

Fractional cover - MODIS, Monthly median composites, Australia coverage


Figure 1: Fractional cover monthly median composite for January 2014

Link to the data

DescriptorData linkLayer name
Dataset digital object identifier (DOI)

GeoNetwork record

Image files
Subsetting Tool (Experimental 'Clip and Ship') Monthly MODIS Fractional Cover
Geoserver derm:aus_modis_monthly_fractional_cover
Timeseries Tool (experimental) 

Data licence and Access rights

RightsCopyright 2013-2014 NSW Office of Environment and Heritage. Rights owned by the NSW Office of Environment and Heritage. Rights licensed subject to Creative Commons Attribution (CC BY).
LicenceCreative Commons Attribution 3.0 License,
AccessThese data can be freely downloaded and used subject to the CC BY licence. Attribution and citation is required as described at We ask that you send us citations and copies of publications arising from work that use these data.

Abstract or Summary

Each image is a composite of all MODIS fractional cover images for the month. The input images are version 2.2 or version 3.0.1 of the CSIRO fractional cover product (Guerschman 2009, Guerschman 2012). The medoid method of Flood (2013) was used to create the composites.

These data are used in support of the NSW DustWatch project,

Spatial and Temporal extents

Spatial resolution (metres)500 m
Spatial coverage (degrees)110.000000 to 155.001329 E, -10.000000 to -45.000512 N
Temporal resolutionMonthly
Temporal coverage2000-03 to ongoing
Sensor & platformMODIS Terra&Aqua
Spatial representation typegrid
Spatial reference systemWGS 84

Point of contact

NameTony Gill
OrganisationNSW Office of Environment and Heritage
PositionRemote Sensing Scientist

Credit [optional]

We thank CSIRO for making the fractional cover data publicly available.


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

Fractional cover data quality is subject to the data quality data quality constraints of the MODIS Fractional Cover product.

Potential data quality issues arising from the medoid method are described in Flood et al 2013.

Validation status

Validation of the fractional cover data is subject to the validation performed for the MODIS Fractional Cover product.

Validation of the medoid method is described in Flood et al 2013.

Related products

ItemProduct link
MODIS Fractional CoverFractional cover - MODIS, CSIRO Land and Water algorithm, Australia coverage
MODIS Fractional Cover metricsFractional cover metrics - MODIS, ABARES algorithm, Australia coverage
Landsat Seasonal Fractional CoverFractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Australia coverage


ItemDetail or link
PublicationGill, T., Heidenreich, S., Guerschman, J P. (2014). MODIS Monthly Fractional Cover: Product Creation and Distribution. Joint Remote Sensing Research Program Publication Series. Available at:
PublicationJuan Pablo Guerschman, Michael J. Hill, Luigi J. Renzullo, Damian J. Barrett, Alan S. Marks, Elizabeth J. Botha (2009). Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors Remote Sensing of Environment, Volume 113, Issue 5, pp. 928-945.
PublicationFlood (2013), Seasonal composite Landsat TM/ETM+ images using the medoid (a multi-dimensional median), Remote Sensing, 5(12), pp. 6481-6500,

Validation report Guerschman, JP, Oyarzabal, M, Malthus, TJ, McVicar, TM, Byrne, G, Randall, LA and Stewart, JB (2012), Validation of the MODIS-based vegetation fractional cover product, CSIRO Land and Water Science Report, Canberra, April 2012, available at

Algorithm summary

Updated algorithm information can be found in the README file,

Three products are provided for each version. These are identified by a three-character product 'stage' code in the file name.

For version 2.2 the stages are:

  • ba2 - monthly composites
  • ba4 - gap filled composites
  • ba5 - a code layer for the gap-filled images.

For version 3.0.1 the stage codes are:

  • bb1 - monthly composites
  • bb3 - gap filled composites
  • bb4 - a code layer for the gap-filled images.

For ba2, bb1, ba4 and bb3 (monthly and gap-filled composites) the following Band labels apply:

  • Band 1 = percent bare (BS)
  • Band 2 = percent photosynthetic vegetation (PV)
  • Band 3 = percent non-photosynthetic vegetation (NPV)
  • Null Pixel value = 255

Stages ba2 or bb1: Monthly fractional cover

The medoid method of Flood (2013) was used to create the composites. The medoid has two steps. For each grid cell:

  1. Find the median cover value for each layer (BS, PV, and NPV). This is a multi-dimensional median.
  2. Find the pixel from the input images that is closest to the multi-dimensional median.

This method has the advantage that the output pixel is one of the input pixels. This is important for the fractional cover as the sum of the percent covers is normally 100, and we wish to retain this in the composites. We compute a medoid where there is at least one non-null pixel from the input images.

For each month number below, the day of year (doy) images listed were used to create the composites:

Month numberInput DoY listNotes
1361 001 009 017Day 361 of the year before
2025 033 041 049
3057 065 073
4081 089 097 105
5113 121 129 137
6145 153 161 169
7177 185 193 201
8209 217 225 233
9241 249 257
10265 273 281 289
11297 305 313 321
12329 337 345 353

Stages ba4 or bb3: gap-filled composites

The monthly composites may contain missing values. This is likely for cloudy areas where there are no valid observations within the month. Depending on the application, it may be desirable to have images without data gaps.

The gap-fill algorithm fills null values with a resampled pixel at a coarser resolution. The resolution of the resampled image is made progressively coarser until all pixels are filled. The resampled pixel sizes are the original resolution multiplied by a power of 2. For example, if res is the original pixel size then the resampled pixel sizes are: res*21, res*22, res*23, ..., res*2n.

While this method is simple, it has the disadvantage that the sum-to-one constraint is lost, i.e. the sum of the percent covers may no longer be 100 where a pixel has been filled.

Stages ba5 or bb4: A code (flag) layer corresponding to the gap-filled composites

The gap-filled code image identifies those pixels in the gap-filled product that had to be filled. A non-zero pixel value represents a filled pixel. The value itself is the resolution of the resampled image used to fill the pixel, expressed as the power of 2 used. For example if the resolution was res*24, then the code value is 4. A value of zero represents a pixel that was not filled. The null value (ocean pixels) are set to 255.

Product version history

Version labelDetail
1.0Initial release

Other items [optional]

Metadata history

2014-07-01Metadata creation date
2014-07-30Added version 3.0.1 based composites to the server and updated the abstract.
Filled out the Algorithm section with details from the README.
Created by Matt Paget on 2014/07/01 13:20

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