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Data

Seasonal Fractional Cover - Landsat, JRSRP Algorithm, Australia Coverage 

Ver: 1.0
Status of Data: superseded
Update Frequency: notPlanned
Security Classification: unclassified
Dataset Published: 2021-09-13
Dataset Last Modified: 2026-08-18
Metadata viewed 33826 times
Dataset accessed 1709 times
Metadata Created: 2013-11-25
Metadata Last Modified: 2026-08-19
How to cite this collection:
Joint Remote Sensing Research Program (2021). Seasonal Fractional Cover - Landsat, JRSRP Algorithm, Australia Coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/f0c32576-9ad7-4c9c-9aa9-22787867e28b 
Data can be accessed from the following links:
HTTPSeasonal Fractional Cover via HTTP
HTTPseasonal_fractional_cover_band_descriptions_and_fil_4ygcbjt.txt
HTTPVegmachine Timeseries Viewer
HTTPPoint-of-truth metadata URL
HTTPro-crate-metadata.json
This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/TERN/0997cb3c-e2e2-45be-ac82-f5e13d24331c. The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. 
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 variability in fractional cover at seasonal (i.e. three-monthly) time scales, forming a consistent time series from 1987 - present. It is useful for investigating inter-annual changes in vegetation cover and analysing regional comparisons. For applications that focus on non-woody vegetation, the ground cover product, derived from fractional cover, may be more suitable. For applications investigating rapid change during a season, monthly composite or single-date (available on request) fractional cover products may be more appropriate. Note: A new fractional cover algorithm will be implemented during 2021, based on additional field validation and a new machine learning approach. 
Lineage
Data not provided. 
Method DocumentationData not provided.
Procedure StepsData not provided.
Spatial Description
Australia 
Temporal Coverage
From 1987-12-01 to on going 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
1) 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. 2) The fractional cover model was compared to samples drawn from 1500 field reference sites. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
1) The USGS aims to provide image-to-image registration with an accuracy of 12m. Refer to the L8 Data Users Handbook for more detail. 2) The fractional cover model achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites. 
ANZSRC - FOR
Climate change impacts and adaptation
Environmental management
Data Stream
Satellite Remote Sensing
GCMD Sciences
LAND USE/LAND COVER
SOILS
VEGETATION - VEGETATION COVER
Horizontal Resolution
30 meters - < 100 meters
Instruments
ETM+
OLI
TM
Parameters
bare soil fraction
non-photosynthetic vegetation fraction
photosynthetic vegetation fraction
vegetation area fraction
Platforms
LANDSAT-5
LANDSAT-7
LANDSAT-8
Project
TERN Landscapes
Temporal Resolution
Weekly - < Monthly
Topic
environment
imageryBaseMapsEarthCover
Author
Joint Remote Sensing Research Program
Contact Point
Data Enquiries, Earth Observation and Social Sciences (EOSS)
Publisher
Terrestrial Ecosystem Research Network
de Vries, C., Danaher, T., Denham, R., Scarth, P. & Phinn, S. (2007). An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites, Remote Sensing of Environment, vol. 107, no. 3, pp. 414-429.
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
Armston, J. D., Danaher, T.J., Goulevitch, B. M., and Byrne, M. I., (2002). Geometric correction of Landsat MSS, TM, and ETM+ imagery for mapping of woody vegetation cover and change detection in Queensland.
Robertson, P (1989). Spatial Transformations for Rapid Scan-Line Surface Shadowing. IEEE Computer Graphics and Applications, vol. 9. doi: 10.1109/38.19049
Danaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., and Gillingham, S. (2010). Ecosystem Function in Savannas: Measurement and Modelling at Landscape to Global Scales.
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. doi:10.3390/rs5010083
Flood, N. (2013) Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-dimensional Median). Remote Sens. 2013, 5(12), 6481-6500
Scarth, P., Röder, A., Schmidt, M., 2010b. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis. In: Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference (ARSPC)
Muir, J. et al (2011), Field measurement of fractional ground cover: supporting ground cover monitoring for Australia. ABARES. Canberra
By Child records
Seasonal Fractional Cover Summary Statistics - Landsat, JRSRP Algorithm Version 3.0, Queensland Coverage
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
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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/ 
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Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
It is not recommended that these data sets be used at scales more detailed than 1:100,000.