Monthly Blended Fractional Cover - Landsat and Sentinel-2, JRSRP Algorithm Version 3.0, Queensland Coverage
The monthly fractional cover product shows representative values for the proportion of bare ground, green and non-green ground cover for Queensland, Australia, from 2015 - present on a monthly basis. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel). This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and Sentinel-2 time series.<br>
A 3 band (byte) image is produced:<br></br>
band 1 - bare ground fraction (in percent),<br></br>
band 2 - green vegetation fraction (in percent),<br></br>
band 3 - non-green vegetation fraction (in percent).<br></br>
The no data value is 255.
Simple
Identification info
- Date (Creation)
- 2022-03-28
- Date (Publication)
- 2022-05-03
- Date (Revision)
- 2025-12-10
- Edition
- 3.0
Publisher
Author
Joint Remote Sensing Research Program
Chancellors Place, St Lucia, Queensland, 4072, Australia
St Lucia
Queensland
4072
Australia
Author
Department of the Environment, Tourism, Science and Innovation, Queensland Government
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
Dutton Park
Queensland
4102
Australia
- Website
- https://www.tern.org.au/
- Purpose
- <br>This product captures variability in fractional cover at monthly time scales, forming a consistent time series from 2015 - present. It is useful for investigating more rapid changes than the three-month seasonal products. For example, the monthly dataset is used by the Queensland pastoral industry for improved monitoring of drought conditions. The green and non-green fractions may include a mix of woody and non-woody vegetation. For applications investigating long-term dynamics, the three-month seasonal product may be more appropriate.</br> <br>This product is based upon the JRSRP Fractional Cover 3.0 algorithm.</br>
- Credit
- 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.
- Credit
- <p></p>This dataset was produced by the Joint Remote Sensing Research Program using data sourced from US Geological Survey and the European Space Agency.
- Status
- On going
Point of contact
Department of the Environment, Tourism, Science and Innovation, Queensland Government - Data Enquiries, Earth Observation and Social Sciences (EOSS) ()
41 Boggo Road, Dutton Park, 4102, Queensland, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Spatial resolution
- Spatial resolution
- 30
- Topic category
-
- Environment
- Imagery base maps earth cover
Extent
- Description
- Queensland, Australia
N
S
E
W
))
Temporal extent
- Time period
- 2015-12-01
- Title
- Beutel Terrence S. et al (2019) VegMachine.net. online land cover analysis for the Australian rangelands. The Rangeland Journal 41
- Website
-
Beutel Terrence S. et al (2019) VegMachine.net. online land cover analysis for the Australian rangelands. The Rangeland Journal 41
Related documentation
- Title
- Sentinel 2 Level 1C Processing
- Website
-
Sentinel 2 Level 1C Processing
Related documentation
- Title
- Sentinel 2 Data Product Quality Reports
- Website
-
Sentinel 2 Data Product Quality Reports
Related documentation
- Maintenance and update frequency
- Monthly
- GCMD Science Keywords
- ANZSRC Fields of Research
- TERN Platform Vocabulary
- TERN Instrument Vocabulary
- TERN Parameter Vocabulary
- QUDT Units of Measure
- GCMD Horizontal Resolution Ranges
- GCMD Temporal Resolution Ranges
Resource constraints
- Use limitation
- The Creative Commons Attribution 4.0 International (CC BY 4.0) license allows others to copy, distribute, display, and create derivative works provided that they credit the original source and any other nominated parties. Details are provided at https://creativecommons.org/licenses/by/4.0/
- File name
- 88x31.png
- File description
- CCBy Logo from creativecommons.org
- File type
- png
- Title
- Creative Commons Attribution 4.0 International Licence
- Alternate title
- CC-BY
- Edition
- 4.0
- Access constraints
- License
- Use constraints
- Other restrictions
- Other constraints
- 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. <br />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. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting
- Other constraints
- <p>It is not recommended that these data sets be used at scales more detailed than 1:100,000.</p>
Resource constraints
- Classification
- Unclassified
- Supplemental Information
- <p>Data are available as cloud optimised GeoTIFF (COG) files. COG files are easier and more efficient for users to access data corresponding to particular areas of interest without the need to download the data first.</p>
Distribution Information
- Distribution format
-
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- OnLine resource
- GitLab Code for Fractional Cover version 3
- OnLine resource
-
monthly_fractional_cover_v3
Monthly Blended Fractional Cover, v3
- OnLine resource
- Landscape Data Visualiser - Monthly Blended Fractional Cover - Landsat and Sentinel-2, JRSRP Algorithm Version 3.0, Queensland Coverage
- OnLine resource
- Vegmachine Timeseries Viewer
- OnLine resource
- ro-crate-metadata.json
Data quality info
- Hierarchy level
- Dataset
- Other
- 1) All the data described here has been generated from the analysis of Level 1A Landsat OLI and Sentinel Level 1C (see Publications: Flood (2017). </br> 2) The fractional cover model was compared to samples drawn from approximately 4000 field reference sites.
- Title
- European Space Agency. (n.d.). Sentinel 2 Performance and Data Quality Reports. SentiWiki.
- Abstract
- European Space Agency. (n.d.). Sentinel 2 Performance and Data Quality Reports. SentiWiki.
Report
Result
- Statement
- 1) The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m. The USGS aims to provide Landsat image-to-image registration with an accuracy of 12 m. </br> 2) The fractional cover model predicts the vegetation cover fractions with MAE/wMAPE/RMSE of:</br> bare - 6.9%/34.9%/14.5% </br> PV - 4.6%/37.9%/10.6% </br> NPV - 9.8%/25.2%/16.9% </br>
Resource lineage
- Statement
- Summary of processing:<br> Landsat 8-9/Sentinel-2 surface reflectance data > multiple single-date fractional cover datasets > monthly composite of fractional cover<br> Further details are provided in the Methods section.
- Hierarchy level
- Dataset
- Title
- Flood, N. (2017). Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sensing, 9(7). https://doi.org/10.3390/rs9070659
- Website
-
https://doi.org/10.3390/rs9070659
Method documentation
- Title
- Flood, N. (2013). Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median). Remote Sensing, 5(12), 6481–6500. https://doi.org/10.3390/rs5126481
- Website
-
https://doi.org/10.3390/rs5126481
Method documentation
- Title
- Flood, N., Danaher, T., Gill, T., & Gillingham, S. (2013). An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing, 5(1), 83–109. https://doi.org/10.3390/rs5010083
- Website
-
https://doi.org/10.3390/rs5010083
Method documentation
- Title
- Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/J.RSE.2011.10.028
- Website
-
https://doi.org/10.1016/J.RSE.2011.10.028
Method documentation
- Title
- European Space Agency. (n.d.). Sentinel 2 Level-1C Algorithms and Products. Sentinel Online.
- Website
-
https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-1c-algorithms-products
Method documentation
Process step
- Description
- <p>Image Preprocessing:<br> Landsat 8 and 9 imagery rated as less than 80% cloud cover was downloaded from the USGS EarthExplorer website as level L1T imagery. 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).</p>
Process step
- Description
- <p>Fractional Cover Model:<br> A multilayer perceptron (MLP) model is used to estimate percentage cover in three fractions – bare ground, photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) from surface reflectance, for every image captured within the season. The MLP model was trained with Tensorflow using Landsat TM, ETM+ and OLI surface reflectance and a collection of approximately 4000 field observations of overstorey and ground cover. The field observations covered a wide variety of vegetation, soil and climate types across Australia, collected between 1997 and 2018 following the procedure outlined in Muir et al (2011). As the model is trained on Landsat imagery, the Sentinel-2 reflectance values are slightly adjusted to more closely resemble Landsat imagery, then the fractional cover model is applied. The model was assessed to predict the vegetation cover fractions with MAE/wMAPE/RMSE of:<br> bare - 6.9%/34.9%/14.5%<br> PV - 4.6%/37.9%/10.6% <br> NPV - 9.8%/25.2%/16.9%.</p>
Process step
- Description
- <p>Data compositing:<br> The method of compositing used selection of representative pixels through the determination of the medoid (multi-dimensional equivalent of the median) of at least three observations of fractional cover imagery. 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. It is robust against extreme values, inherently avoiding the selection of outliers, such as occurs when cloud or cloud shadow goes undetected. 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 monthly fractional cover image.<br> For further details on this method see Flood (2013).</p>
Reference System Information
- Reference system identifier
- EPSG/EPSG:3577
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/8d3c8b36-b4f1-420f-a3f4-824ab70fb367
- Title
- TERN GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
Point of contact
Type of resource
- Resource scope
- Dataset
- Metadata linkage
-
https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/8d3c8b36-b4f1-420f-a3f4-824ab70fb367
Point-of-truth metadata URL
- Date info (Creation)
- 2022-03-28T00:00:00.000000+00:00
- Date info (Revision)
- 2025-12-10T09:47:31.665088+00:00
Metadata standard
- Title
- ISO 19115-1:2014/AMD 1:2018 Geographic information - Metadata - Fundamentals
- Edition
- 1
Metadata standard
- Title
- ISO/TS 19115-3:2016
- Edition
- 1.0
Metadata standard
- Title
- ISO/TS 19157-2:2016
- Edition
- 1.0
- Title
- Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
- Date (published)
- 2021
- Edition
- 1.0
Identifier
Overviews
Spatial extent
N
S
E
W
Provided by
Associated resources
Not available