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Fractional Cover - MODIS, CSIRO algorithm

Ver: 1.0
Status of Data: on going
Update Frequency: monthly
Security Classification: unclassified
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Description

Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series. Monthly: The monthly product is aggregated from the 8-day composites using the medoid method. Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series. MODIS fractional cover has been validated for Australia.

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.
Funding was provided by CSIRO (Australia), Terrestrial Ecosystem Research Network (TERN) (Australia), and the Australian Government Department of Agriculture.

Purpose
This project will bring the enhancements to the ground cover monitoring tool (the RaPP Map) needed to assist in enabling the NRM agencies and individuals to undertake monthly reporting on ground cover level change. Proposed enhancements include: Continuity of the tool data quality increase the number of outputs required by NRMs improve the functionality as determined by the users This project is closely aligned with the Ground cover training and target setting (GTTS) project being proposed by John Leys from the NSW OEH which focuses on the training in the use of the tool to NRM agencies. This project is also contributing to the international engagement via the GEOGLAM initiative which aims at improving the information on agricultural productivity.

Lineage
Monthly vegetation cover is calculated from the 8-day composites using a medoid method as described in Gill et al. Monthly anomalies show the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available\nMonthly deciles show the decile (ranking) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time series. Version 3.0: Fractional cover was derived using a linear unmixing methodology (Guerschman et al. 2015). Version 3.1: ("v310") Same as Version 3.0 with the following modifications: 1- input data changed to MODIS Collection 6.0 (MCD43A4.006) (see source). 2- calibration dataset expanded to include ~3022 field measurement sites accross Australia. 3- overall accuracy improved to an RMSE of 11.3%, 16.1% and 14.7% for the PV, NPV and BS fractions respectively.

Data Creation
Time Period Composition Mediod Method: The medoid is a multi-dimensional analogue of the median. It can be applied to creating a time-period composite of images, for example a seasonal composite, by selecting a single date for each pixel that minimizes the sum of the distances to the band pixel values of all other dates in the set. Source: Flood, N. (2013). Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median). https://doi.org/10.3390/rs5126481
Linear Spectral Unmixing Method: Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into a set of endmembers representing the types of objects present in the pixel, and their corresponding abundances. Source: Shi, C., & Wang, L. (2014). Incorporating spatial information in spectral unmixing: A review. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2014.03.034

Temporal coverage

From 1/1/2001

Dates

Creation date

11/11/2019

Publication date

3/10/2020

Revision date

9/24/2024

Citation information

How to cite this collection:

Guerschman, J. P. (2020): Fractional Cover - MODIS, CSIRO algorithm. Version 1.0. Terrestrial Ecosystem Research Network. (Dataset). https://portal.tern.org.au/metadata/TERN/a6ef2480-e397-4c42-9706-3865dd6ca380

Rights and Licensing

CC-BY

Creative Commons Attribution 4.0 International Licence
http://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
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.

Keywords

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GCMD Science
ANZSRC - FOR
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Temporal Resolution
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Publications

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Data Quality

Data QualityData Quality
  • Data Quality Assessment Scope:
    Version 3.0: Fractional cover was derived using a linear unmixing methodology (Guerschman et al. 2015). The method uses all 7 MODIS bands and adds log transforms and band interaction terms to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. The calibration and validation steps used 1171 field observations across Australia.
  • Data Quality Assessment Result:
    Overall, the model fitted and applied to MCD43A4 fractional cover has a root mean square error (RMSE) of 12.9%, 18.1% and 16.6% for the PV, NPV and BS fractions respectively (percentage cover) Version 3.1: ("v310") Same as Version 3.0 with the following modifications: 1- input data changed to MODIS Collection 6.0 (MCD43A4.006) (see source). 2- calibration dataset expanded to include ~3022 field measurement sites accross Australia. 3- overall accuracy improved to an RMSE of 11.3%, 16.1% and 14.7% for the PV, NPV and BS fractions respectively.

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Contacts
  • Terrestrial Ecosystem Research Network
  • Building 1019, 80 Meiers Rd
  • QLD 4068
  • Australia
Spatial coverage
Spatial description
Global dataset on fractional cover, CSIRO algorithm.