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Seasonal Surface Reflectance - Landsat, JRSRP Algorithm, Australia Coverage 

Ver: 1.0
Status of Data: onGoing
Update Frequency: quarterly
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 2594 times
Accessed 227 times
Dataset Created: 2014-06-20
Dataset Published: 2021-04-09
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Seasonal Surface ReflectanceWMSlandsat_seasonal_surface_reflectanceHTTPLandscape Data Visualiser - Seasonal surface reflectance - LandsatHTTPseasonal_surface_reflectance_landsat_filenaming_con_uSVOxS8.txtHTTPro-crate-metadata.json
How to cite this collection:
Department of the Environment, T. (2021). Seasonal Surface Reflectance - Landsat, JRSRP Algorithm, Australia Coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/5a31eed4-e43a-404d-b534-3f820305ed61 
The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+/OLI 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. 
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 US Geological Survey. 
Purpose
This product captures surface reflectance at seasonal (ie three-monthly) time scales, forming a consistent time series from 1987 - present. For applications that focus on vegetation changes, the fractional cover and ground cover products may be more suitable.  
Lineage
Landsat imagery < 80% cloud cover downloaded > corrections and masking > medoid calculated > seasonal surface reflectance 
Method DocumentationData not provided.
Procedure Steps

1. 

Image Pre-Processing: All input Landsat TM/ETM+/OLI imagery was downloaded from the USGS EarthExplorer website as level L1T imagery. Images which the EarthExplorer site rated as having greater than 80% cloud cover were not downloaded, on the assumption that they would contribute little, and would add extra noise in the form of undetected cloud, shadow, and mis-registration (which is a greater risk in very cloudy images). The imagery has been corrected for atmospheric effects, and bi-directional and topographic effects, using the methods detailed by Flood et al. (2013). 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. 

2. 

Seasonal Compositing: The seasonal composites were calculated using the medoid in the 6-dimensional space of reflectance values from the six Landsat reflective bands. The medoid is a “measure of centre” of a multi-variate set of points, similar in nature to the median of a univariate dataset. The medoid has similar desirable properties to the univariate median. 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. Two variations of seasonal surface reflectance are available and can be distinguished by the ‘satellite category code’ component of the filename. (Refer to file naming convention information). ‘lz’ indicates that the composite was derived from all possible Landsat data, and ‘l8’ indicates that the composite was derived from Landsat8 OLI data only. For the latter case, the OLI Band 1 is omitted so that the resulting composite has the same bands as the generic Landsat ‘lz’ variant. 

3. 

Image Restrictions: In estimating a value which is representative of the season, we choose to add a restriction on the number of input points. 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. Neither the medoid nor the geometric median are robust against a single outlier in the case of less than three observations. 

Australia
Temporal Coverage
From 1987-12-01 to on going 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
The input imagery was processed to level L1T by the USGS. Geodetic accuracy of the product depends on the image quality and the accuracy, number, and distribution of the ground control points. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail. 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
GCMD Sciences
BIOSPHERE - VEGETATION COVER
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
OLI
TM
Parameters
at-surface reflectance
Platforms
LANDSAT-5
LANDSAT-7
LANDSAT-8
Temporal Resolution
Weekly - < Monthly
Topic
environment
imageryBaseMapsEarthCover
Author
Department of the Environment, Tourism, Science and Innovation, Queensland Government
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 (2012) 83-94.
Flood, N., Danaher, T., Gill, T. and Gillingham, S. (2013) An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sens. 2013, 5(1), 83-109
Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500
Supplemental Information
The standard form of the file naming convention is: _ _ _ _ For this dataset are (comma-separated): Data Naming Element, Possible Code(s), Descriptor Standard Elements satellite category, lz, Landsat - all possible, l8, Landsat 8, instrument, tm, thematic, ol, operational land imager product, re, reflective where, qld,nsw,vic,tas,sa,nt,wa, state when , myyyymmyyyymm , season start date (1st day of month) and season end date (last day of month)  processing stage, dbi, seasonal surface reflectance, data projection, a2, Australian Albers Equal Area 
Resource Specific Usage
Data not provided. 
Environment Description
Data not provided. 
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Terrestrial Ecosystem Research Network
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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/
Copyright 2010-2021. JRSRP. Rights owned by the Joint Remote Sensing Research Project (JRSRP). 
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. 
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. 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. 
It is not recommended that these data sets be used at scales more detailed than 1:100,000. 

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Version:6.2.22