Seasonal Fractional Cover - Sentinel-2, JRSRP Algorithm Version 3.0, Eastern and Central Australia Coverage
<p>The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover, created from a time series of Sentinel-2 imagery. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10 m per-pixel) for each 3-month calendar season across Eastern and Central Australia from 2016 to present. The green and non-green fractions may include a mix of woody and non-woody vegetation.</p>
<p>This model was originally developed for Landsat imagery, but has been adapted for Sentinel-2 imagery to produce a 10 m resolution equivalent product.</p>
<p>A 3 band (byte) image is produced:</p>
<ul>
<li>band 1 - bare ground fraction (in percent),</li>
<li>band 2 - green vegetation fraction (in percent),</li>
<li>band 3 - non-green vegetation fraction (in percent).</li>
</ul>
<p>The no data value is 255.</p>
Simple
Identification info
- Date (Creation)
- 2022-03-28
- Date (Publication)
- 2022-05-03
- Date (Revision)
- 2024-09-25
- 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 Environment and Science (2017-2023), Queensland Government
41 Boggo Road, Dutton Park, Queensland, 4102, Australia
Dutton Park
Queensland
4102
Australia
- Website
- https://www.tern.org.au/
- Purpose
- <p>This product captures variability in fractional cover at seasonal (i.e. three-monthly) time scales, forming a consistent time series from late 2015 - present. It is useful for investigating recent inter-annual changes in vegetation cover and analysing regional comparisons. For applications that focus on non-woody vegetation, the Landsat-derived ground cover product may be more suitable. For applications investigating rapid change during a season, the monthly composite or single-date (available on request) fractional cover products may be more appropriate.</p> <p>This product is based upon the JRSRP Fractional Cover 3.0 algorithm.</p>
- 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 the European Space Agency (ESA) Copernicus Sentinel Progam.
- Status
- On going
Point of contact
Department of Environment and Science (2017-2023), Queensland Government - van den Berg, Deanna (Principal Scientist (Remote Sensing))
41 Boggo Road, Dutton Park, Queensland, 4102, Australia
41 Boggo Road
Dutton Park
Queensland
4102
Australia
Spatial resolution
- Spatial resolution
- 10
- Topic category
-
- Environment
- Imagery base maps earth cover
Extent
- Description
- Australia excluding Western Australia and South Australia
N
S
E
W
))
N
S
E
W
))
Temporal extent
- Time period
- 2015-12-01
- 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
- Title
- Beutel, T. S., Trevithick, R., Scarth, P., & Tindall, D. (2019). VegMachine.net. online land cover analysis for the Australian rangelands. The Rangeland Journal, 41(4), 355–362. https://doi.org/10.1071/RJ19013
- Website
-
Beutel, T. S., Trevithick, R., Scarth, P., & Tindall, D. (2019). VegMachine.net. online land cover analysis for the Australian rangelands. The Rangeland Journal, 41(4), 355–362. https://doi.org/10.1071/RJ19013
Related documentation
- Maintenance and update frequency
- Quarterly
- 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
- 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.
Distribution Information
Distributor
Distributor
- Distribution format
-
- NetCDF
- OnLine resource
- Differences between Fractional Cover version 2 and version 3
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- Distribution format
-
- OnLine resource
- GitLab Code for Fractional Cover version 3
- OnLine resource
-
Seasonal Fractional Cover - Sentinel-2, v3.0
sentinel_fractional_v3
- OnLine resource
- Landscape Data Visualiser - Seasonal Fractional Cover - Sentinel-2, JRSRP Algorithm Version 3.0, Eastern and Central Australia Coverage
- 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 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.<br> 2) The fractional cover model was compared to samples drawn from approximately 4000 field reference sites.
- Title
- Sentinel 2 Performance and Data Quality Reports
- Abstract
- Sentinel 2 Performance and Data Quality Reports
Report
Result
- Statement
- 1) The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order 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
- <p>Summary of processing:<br> Sentinel 2 surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover <br />Further details are provided in the Methods section.</p>
- Hierarchy level
- Dataset
- Title
- Sentinel 2 Level 1C Algorithms and Products
- Website
-
https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-1c-algorithms-products
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
- 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
- Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., & Stewart, J. B. (2011). Field measurement of fractional ground cover: A technical handbook supporting ground cover monitoring for Australia.
- Website
-
https://www.researchgate.net/publication/236022381_Field_measurement_of_fractional_ground_cover
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
Process step
- Description
- Image Pre-processing:<br> 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).
Process step
- Description
- 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> photosynthetic vegetation (PV) - 4.6%/37.9%/10.6%<br> non-photosynthetic vegetation (NPV) - 9.8%/25.2%/16.9%.
Process step
- Description
- Data Compositing:<br> The method of compositing selected 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 seasonal fractional cover image. For further details on this method see Flood (2013).
Reference System Information
- Reference system identifier
- EPSG/EPSG:3577
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/13810293-c6b5-442b-bfcd-817700738e0d
- 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/13810293-c6b5-442b-bfcd-817700738e0d
Point-of-truth metadata URL
- Date info (Creation)
- 2022-03-28T00:00:00
- Date info (Revision)
- 2024-09-25T00:00:00
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
N
S
E
W
Provided by
Associated resources
Not available