Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage
The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. This dataset is intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. The two 20m bands are resampled using cubic convolution.
The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.
Simple
Identification info
- Date (Creation)
- 2018-01-20
- Date (Publication)
- 2021-09-23
- Date (Revision)
- 2024-10-21
- Edition
- 1.0
Publisher
Author
Joint Remote Sensing Research Program
Chancellors Place, St Lucia, Queensland, 4072, Australia
St Lucia
Queensland
4072
Australia
- Website
- https://www.tern.org.au/
- Purpose
- This product captures surface reflectance at seasonal (ie three-monthly) time scales, forming a consistent time series from late 2015 - present. For applications that focus on vegetation changes, the fractional cover and ground cover products may be more suitable. For longer time periods, the Landsat-derived products may be more suitable.
- 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
- 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
- Topic category
-
- Environment
Extent
- Description
- Australia excluding Western Australia and South Australia
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Temporal extent
- Time period
- 2015-12-01
- Title
- 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
- Website
-
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
Related documentation
- Title
- Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118. doi:10.1016/j.rse.2011.10.028
- Website
-
Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118. doi:10.1016/j.rse.2011.10.028
Related documentation
- Title
- Sentinel 2 Level 1C Processing
- Website
-
Sentinel 2 Level 1C Processing
Related documentation
- Title
- Flood, N. (2017) Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens. 9, no. 7. doi:10.3390/rs9070659
- Website
-
Flood, N. (2017) Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia. Remote Sens. 9, no. 7. doi:10.3390/rs9070659
Related documentation
- Title
- Sentinel 2 Data Product Quality Reports
- Website
-
Sentinel 2 Data Product Quality Reports
Related documentation
- Title
- Gill, T. et al (2017) A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38:3. doi: 10.1080/01431161.2016.1266112
- Website
-
Gill, T. et al (2017) A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series, International Journal of Remote Sensing, 38:3. doi: 10.1080/01431161.2016.1266112
Related documentation
- Title
- Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
- Website
-
Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
Related documentation
- Title
- Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications, vol. 9. doi: 10.1109/38.19049
- Website
-
Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications, vol. 9. doi: 10.1109/38.19049
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
- Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.
- Other constraints
- Copyright 2010-2020. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP).
- Other constraints
- While 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.
- Other constraints
- It is not recommended that these data sets be used at scales more detailed than 1:100,000.
Resource constraints
- Classification
- Unclassified
- Supplemental Information
- The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.
Distribution Information
Distributor
Distributor
- Distribution format
-
- NetCDF
Distribution Information
Distributor
Distributor
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
Indooroopilly
Queensland
4068
Australia
- Distribution format
-
- OnLine resource
- Cloud Optimised GeoTIFFs - Seasonal surface reflectance
- OnLine resource
- Landscape Data Visualiser
- OnLine resource
-
Seasonal Surface Reflectance - Sentinel-2
sentinel_seasonal_surface_reflectance
- OnLine resource
- ro-crate-metadata.json
Data quality info
- Hierarchy level
- Dataset
- Other
- 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.
Report
Result
- Statement
- The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m.
Resource lineage
- Statement
- Sentinel 2 Level 1C downloaded > Masks applied > Mediod calculated
- Hierarchy level
- Dataset
Process step
- Description
- Image Pre-processing: 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). 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, and the methods described by Robertson (1989).
Process step
- Description
- Seasonal Surface Reflectance: The 6 Landsat-like reflectance bands were stacked together, and the medoid calculated in the resulting 6-dimensional space of reflectance values. The medoid is a “measure of centre” of a multi-variate set of points, similar in nature to the median of a univariate dataset. In a general cluster of points, in n-dimensional space, the medoid will lie roughly in the centre of the cluster, making it a good choice as representative of that set of points. Most importantly, it is robust against the presence of outliers in the set, until at least half of the points are to be considered as outliers, after which it breaks down. If a given pixel has less than three observations available for the season, after masking, we define the result as missing, on the principle that we do not have enough data to know how representative our choice might be. For further details on this method see Flood (2013).
Reference System Information
- Reference system identifier
- EPSG/EPSG:4326
- Reference system type
- Geodetic Geographic 2D
Metadata
- Metadata identifier
-
urn:uuid/0fbb3c7a-0951-4730-ac16-7a2ca4e1bf7e
- 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/0fbb3c7a-0951-4730-ac16-7a2ca4e1bf7e
Point-of-truth metadata URL
- Date info (Creation)
- 2018-01-20T00:00:00
- Date info (Revision)
- 2024-10-21T00:00:00
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
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W
Provided by
Associated resources
Not available