Joint Remote Sensing Research Program & Department of the Environment, T. (2021). Monthly Blended Fractional Cover - Landsat and Sentinel-2, JRSRP Algorithm, Queensland Coverage. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://portal.tern.org.au/metadata/2d52273c-115a-41ca-88f3-d70fb7b8e831
Monthly Blended Fractional Cover - Landsat and Sentinel-2, JRSRP Algorithm, Queensland Coverage
Ver: 1.0
Status of Data: superseded
Update Frequency: notPlanned
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 1367 times
Accessed 202 times
Dataset Created: 2017-01-31
Dataset Published: 2021-04-15
How to cite this collection:
This product has been superseded and will not be processed from early 2023.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/8d3c8b36-b4f1-420f-a3f4-824ab70fb367.
The monthly fractional cover product shows representative values for the proportion of bare ground, green and non-green ground cover across a month. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each month. This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and Sentinel-2 time series.
The monthly fractional cover product shows representative values for the proportion of bare ground, green and non-green ground cover across a month. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each month. This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and Sentinel-2 time series.
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 and the European Space Agency.
Purpose
This product captures variability in fractional cover at monthly time scales, forming a consistent time series from 2015 - present. It is useful for investigating more rapid changes than the three-month seasonal products. For example, the monthly dataset is used by the Queensland pastoral industry for improved monitoring of drought conditions. The green and non-green fractions may include a mix of woody and non-woody vegetation. For applications investigating long-term dynamics, the three-month seasonal product 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
Summary of processing: Landsat 8/Sentinel-2 surface reflectance data > multiple single-date fractional cover datasets > monthly composite of fractional cover
Further details are provided in the Methods section.
Further details are provided in the Methods section.
Method DocumentationData not provided.
Procedure Steps
1.
Image preprocessing:
Landsat 8 imagery rated as less than 80% cloud cover was downloaded from the USGS EarthExplorer website as level L1T imagery. Sentinel-2A 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).
2.
Fractional Cover Model:
The bare soil, green vegetation and non-green vegetation endmembers for the blended Landsat 8 and Sentinel 2 are calculated using models developed for seasonal fractional cover across Australia. Values are reported as percentages of cover plus 100. The fractions stored in the 4 image layers are: Band1 - bare (bare ground, rock, disturbed), Band2 - green vegetation, Band3 - non green vegetation (litter, dead leaf and branches), Band4 - Model fitting error.
3.
Data compositing:
The method of compositing used selection of 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).
Queensland, Australia
Temporal Coverage
From 2015-12-01 to on going
Spatial Resolution
Data not provided.
Vertical Extent
Data not provided.
Data Quality Assessment Scope
1) All the data described here has been generated from the analysis of Level 1A Landsat OLI and Sentinel Level 1C (see Publications: Flood (2017)).
2) The seasonal fractional cover model output was compared to 1500 field sites.
Data Quality Report
Data not provided.
Data Quality Assessment Outcome
1) The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m. The USGS aims to provide Landsat image-to-image registration with an accuracy of 12m.
2) The fractional cover model (based on Landsat) achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites.
Horizontal Resolution
Parameters
Temporal Resolution
Topic
Terrestrial Ecosystem Research Network
80 Meiers Road, Indooroopilly, Queensland, 4068, Australia.
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