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Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage 

Ver: 1.0
Status of Data: onGoing
Update Frequency: quarterly
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 2451 times
Accessed 243 times
Dataset Created: 2018-01-20
Dataset Published: 2021-09-23
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Seasonal surface reflectanceWMSsentinel_seasonal_surface_reflectanceHTTPLandscape Data VisualiserHTTPsentinel2_surface_reflectance_band_and_filenaming-v01.txtHTTPro-crate-metadata.json
How to cite this collection:
Joint Remote Sensing Research Program (2021). Seasonal surface reflectance - Sentinel-2, JRSRP algorithm, Eastern and Central Australia coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/0fbb3c7a-0951-4730-ac16-7a2ca4e1bf7e 
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. 
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. This dataset was produced by the Joint Remote Sensing Research Program using data sourced from the European Space Agency (ESA) Copernicus Sentinel Progam. 
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. 
Lineage
Sentinel 2 Level 1C downloaded > Masks applied > Mediod calculated 
Method DocumentationData not provided.
Procedure Steps

1. 

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). 

2. 

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). 

Australia excluding Western Australia and South Australia
Temporal Coverage
From 2015-12-01 to on going 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
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. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m. 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
GCMD Sciences
LAND SURFACE - LAND USE/LAND COVER
LAND SURFACE - LANDSCAPE ECOLOGY
LAND SURFACE - REFLECTANCE
Horizontal Resolution
30 meters - < 100 meters
Instruments
MSI
Parameters
at-surface reflectance
Platforms
Sentinel-2A
Sentinel-2B
Temporal Resolution
Monthly - < Annual
Topic
environment
Author
Joint Remote Sensing Research Program
Contact Point
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
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
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
Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications, vol. 9. doi: 10.1109/38.19049
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
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
Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
Sentinel 2 Level 1C Processing
Sentinel 2 Data Product Quality Reports
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. 
Resource Specific Usage
Data not provided. 
Environment Description
Data not provided. 
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It is not recommended that these data sets be used at scales more detailed than 1:100,000. 
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}. 
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