Brief description
This product has been superseded and will not be processed from early 2023.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/23881. The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover, created from a time series of Sentinel 2 imagery. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10 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. This model was originally developed for Landsat imagery, but has been adapted for us with Sentinel-2 imagery to produce a 10 m resolution equivalent product.Notes
Supplemental InformationA 4 band (byte) image is produced: band 1 – bare ground fraction (in percent) band 2 - green vegetation fraction (in percent) band 3 – non-green vegetation fraction (in percent) band 4 – Error Layer representing the RMSE between the predicted pixel value and the actual pixel value on a nominal scale of 0 (no error) to 100 (very large error).
Lineage
Summary of processing: Sentinel 2 surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional coverFurther details are provided in the Methods section.
Data Creation
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).
Fractional Cover Model:
The bare soil, green vegetation and non-green vegetation endmembers for the combination of Landsat-5 TM and Landsat-7 are calculated using models linked to an intensive field sampling program whereby more than 1500 sites covering a wide variety of vegetation, soil and climate types were sampled to measure overstorey and ground cover following the procedure outlined in Muir et al (2011). A constrained linear spectral unmixing is applied to the image archive using the derived endmembers and has an overall model Root Mean Squared Error (RMSE) of 11.6%. The model originally developed using Landsat imagery has been adapted for Sentinel-2 imagery using the reflectance adjustment factors calculated by Flood (2017), to ensure consistency of reflectance data between the Landsat and Sentinel-2 instruments.
Data Compositing:
The method of compositing selected 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).
Notes
CreditWe 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.
This product captures variability in fractional cover at seasonal (ie three-monthly) time scales, forming a consistent time series from late 2015 - present. It is useful for investigating recent inter-annual changes in vegetation cover and analysing regional comparisons. For applications that focus on non-woody vegetation, the landsat-derived ground cover product, 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.
Data Quality Assessment Scope
local :
dataset
1) 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.
2) The fractional cover model was compared to samples drawn from 1500 field reference sites.
Data Quality Assessment Result
local :
Quality Result
1) The Sentinel-2 Data Quality Report from ESA indicates that positional accuracy is on the order of 12 m.
2) The fractional cover model (based on Landsat) achieved an overall model Root Mean Squared Error (RMSE) of 11.6% against field reference sites.
Created: 2018-01-20
Issued: 2021-09-23
Modified: 2024-09-24
Data time period: 2015-12-01
text: Australia excluding Western Australia and South Australia
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Point-of-truth metadata URL- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/30aa6403-5efb-47cd-bbbb-652d5c865df8
- global : 30aa6403-5efb-47cd-bbbb-652d5c865df8
