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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.
A 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).
Summary of processing: Sentinel 2 surface reflectance data > multiple single-date fractional cover datasets > seasonal composite of fractional cover
Further details are provided in the Methods section.
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).