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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&nbsp;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

Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd
Indooroopilly
QLD
4068
Australia
+61 7 3365 9097

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
  • LAND USE/LAND COVER
  • VEGETATION COVER
  • SOILS
ANZSRC Fields of Research
  • Environmental management
  • Climate change impacts and adaptation
TERN Platform Vocabulary
  • LANDSAT-8
  • LANDSAT-9
  • Sentinel-2A
  • Sentinel-2B
  • LANDSAT-7
TERN Instrument Vocabulary
  • OLI
  • MSI
TERN Parameter Vocabulary
  • bare soil fraction
  • Percent
  • photosynthetic vegetation fraction
  • Percent
  • non-photosynthetic vegetation fraction
  • Percent
QUDT Units of Measure
  • Percent
  • Percent
  • Percent
GCMD Horizontal Resolution Ranges
  • 30 meters - < 100 meters
GCMD Temporal Resolution Ranges
  • Weekly - < Monthly

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
Linkage
https://w3id.org/tern/static/cc-by/88x31.png

Title
Creative Commons Attribution 4.0 International Licence
Alternate title
CC-BY
Edition
4.0
Website
https://creativecommons.org/licenses/by/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
Cloud Optimised GeoTIFFs - Monthly Fractional Cover for Queensland

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.
Website
https://sentiwiki.copernicus.eu/web/document-library#DocumentLibrary-PerformanceandDataQualityReportsLibrary-S2-Performance-DQR

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&nbsp;m. The USGS aims to provide Landsat image-to-image registration with an accuracy of 12&nbsp;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

Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd
Indooroopilly
QLD
4068
Australia
+61 7 3365 9097

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

Code
10.5281/zenodo.5652221
Website
https://github.com/ternaustralia/TERN-ISO19115/releases/tag/v1.0

 
 

Overviews

Spatial extent

N
S
E
W


Keywords

ANZSRC Fields of Research
Climate change impacts and adaptation Environmental management
GCMD Science Keywords
LAND USE/LAND COVER SOILS VEGETATION COVER

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