<del>LS8_GM_NBART_7_-29_20130101.nc</del>

creation_dt 2018-03-12T08:26:33.408976 creation_time 2018-03-12 08:26:33 format NetCDF id 31b8c37d-d699-4ff6-82e7-6a43983179f7 instrument OLI label • lat -26.587 to -25.644 lon 139.061 to 140.131 platform LANDSAT_8 product_type surface_reflectance_statistical_summary time 2013-01-01 00:00:00 to 2013-01-01 00:00:00

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls8_gm/7_-29/LS8_GM_NBART_7_-29_20130101.nc

Metadata Document 🔗

RAW As Stac

id: 31b8c37d-d699-4ff6-82e7-6a43983179f7

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creation_dt: 2018-03-12T08:26:33.408976

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algorithm:

version: 0.9a8

repo_url: https://github.com/GeoscienceAustralia/agdc_statistics.git

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location: /g/data/u46/users/ia1511/Work/data/geomedian/output

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x: 2000

y: 2000

date_ranges:

end_date: 2016-01-01

step_size: 1y

start_date: 2013-01-01

stats_duration: 1y

output_products: [

metadata:

platform: LANDSAT-8

statistic: new_geomedian

product_type: surface_reflectance_statistical_summary

output_params:

zlib: True

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file_path_template: ls8_gm/ls8_gm_nbart_{x}_{y}_{epoch_start:%Y%m%d}.nc

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global_attributes:

title: Surface Reflectance Geometric Median

source: geometric median v2017 of surface reflectance NBAR+T

license: CC BY Attribution 4.0 International License

summary: The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a geomedian image is the statistical median of all observations for that pixel from a period of time. For example the 2016 Landsat 8 geomedian image over an area will be the median of Landsat 8 pixels observed for that area in 2016. The geometric median products are available as annual datasets for years since 1987. Each annual composite is created from one calendar year of surface reflectance and for one sensor, i.e. Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) or Operational Land Imager (Landsat 8). The algorithm considers concurrent spectral measurements as a multi-dimensional set and outputs a multi-dimensional median that maintains the relationships among the measurements. This provides a median of the physical conditions measured by the earth observations used to create it, and provides a good representation of a typical pixel observation devoid of outliers. More information on the algorithm can be found in the article below: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896

keywords: AU/GA,NASA/GSFC/SED/ESD/LANDSAT,REFLECTANCE,ETM+,TM,OLI,EARTH SCIENCE

platform: LANDSAT-5,LANDSAT-7,LANDSAT-8

instrument: TM,ETM+,OLI

references: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896

description: Surface Reflectance Geometric Median 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577)

institution: Commonwealth of Australia (Geoscience Australia)

cdm_data_type: Grid

product_suite: Surface Reflectance Geometric Median 25m

publisher_url: http://www.ga.gov.au

acknowledgment: - Landsat data is provided by the United States Geological Survey (USGS) through direct reception of the data at Geoscience Australias satellite reception facility or download.

publisher_name: Section Leader, Operations Section, NEMO, Geoscience Australia

product_version: 2

publisher_email: earth.observation@ga.gov.au

keywords_vocabulary: GCMD

coverage_content_type: modelResult

machine:

uname: Linux r3591 3.10.0-693.17.1.el6.x86_64 #1 SMP Fri Feb 9 03:25:58 UTC 2018 x86_64

hostname: r3591

software_versions:

python:

version: 3.6.3 | packaged by conda-forge | (default, Nov 4 2017, 10:10:56) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]

datacube:

version: 1.5.1+421.g2306bf4

repo_url: https://github.com/opendatacube/datacube-core.git

source_datasets:

statistics:

step: 1y

period: 1y